心肺复苏自动化过程中的关键算法研究
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摘要
心脏骤停(cardiac arrest，CA)，又称心源性猝死(sudden death)是指心脏的机械活动停止，同时左心室收缩不足或停止收缩。在美国每年大约有22.5万人死于院外(Out-of-hospital)心源性猝死，同时每年约有37～75万住院病人因心脏骤停实施心肺复苏术(cardiopulmonary resuscitation，CPR)。我国虽然还没有确切的心源性猝死的流行病学资料，但专家估计这个数字会达到每年600万人。
由于心脏骤停常是冠心病的首发表现形式，有效的心肺复苏是抢救这类患者的唯一途径。心肺复苏是通过对心脏骤停的快速识别和积极抢救，人工重建或恢复自主呼吸与循环，避免发生心肺脑功能不全。快速采取基本生命支持(basic life support，BLS)是心肺复苏成功的关键。
心脏骤停有两种不同的形式。一种是由于节律失常引起的心脏骤停(dysarhithmic cardiac arrest)，另一种是因为呼吸停止引起的心脏骤停(asphyxial or respiratory cardiac arrest)。前一种类型的心脏骤停患者大都出现心室颤动(ventricular fibrillation，VF)，而后一类心脏骤停则是由于溺死、药物过量或外伤引起，只有约5％～15％的患者出现心室颤动。对前一类型的患者强调早期除颤和即时的心肺复苏，而对后一种类型的患者则需要实施有效的胸外按压和人工通气治疗。
目前心肺复苏有效的抢救成功率依然很低，只有对其不断改进才有可能降低死亡率，这主要是由于以下三个原因引起的。(1)心肺复苏开始得比较晚，包括胸外按压和人工通气。(2)胸外按压的效率较低以及频繁间断。(3)电击除颤不及时造成抢救时机的延误。
心室颤动如不及时去除可在数分钟内转为心室静止(asystole)。为了增加早期除颤的机会，尽量缩短除颤时间，体外自动除颤仪(automatic external defibrillator, AED)应运而生。AED的最大特点是提高了电击除颤的自动化程度，是专为非医务人员和初级救生员设计使用的，其识别心室颤动的敏感性与特异性均超过94％。抢救人员只要发现患者意识丧失，无脉搏就可将AED置于患者的胸壁上并启动开关，AED感知心电信号，如能识别出室性心动过速(Ventricular Tachycardia，VT)，或心室颤动，就可自动除颤。应用AED后的研究显示，与单纯的基础生命支持相比可明显提高存活率。美国AHA和IAFC已要求每辆急救车和消防车均需配备AED。随着AED的推广和普及，可以期望更多的生命将会被挽救。
由于心脏骤停患者绝大部分(60％-70％)发生在院前，而且在常温下心脏停搏5分钟后脑细胞即可发生不可逆损害，10分钟后脑细胞死亡。在此期间如果不实施心肺复苏术，则心脏的电活动就会逐渐消失，最后出现心室静止，心电图出现一条直线。心肺复苏和药物治疗可能会增加缺血心肌的血液和氧气循环，从而将心室静止转变为心室颤动，而后方可电击除颤。因心室静止和心室颤动期间心脏停止收缩，因此无法检测到脉搏信号。如果心脏只存在心肌的电活动而没有相应的机械收缩，则称为心电机械分离(electromechanical dissociation，EMD)或无脉搏心电活动(pulseless electrical activity, PEA)。这种情况常会出现在药物治疗或心肺复苏但没有实施电击除颤，或由心室颤动转变为心室静止的过程中。
目前心肺复苏有一个标准的操作指南(the international guidelines)，它根据对呼吸、脉搏以及心电节律的检测来确定相应的复苏措施。而目前使用的AED只能根据患者的心电波形做出相应的节律分析决定是否需要电击除颤，其它如呼吸检测、脉搏检查等均需要由目击者或抢救人员来判断。为实现心肺复苏的全自动化，目前仍需要解决以下一些问题：
(1)胸外按压过程中的心电节律识别。心脏骤停患者的存活率随心室颤动持续时间的延长而迅速降低，平均每分钟下降约7％～10％。当施行电击除颤的时间延迟10～12分钟以上时，存活的可能性几乎为零。尽早应用基础的心肺复苏，并尽快实施电击除颤，可有效提高心脏骤停患者的存活率。但目前使用的体外自动除颤仪在实施电击除颤之前需要反复进行节律分析。否则如果将正常的非除颤心室搏动节律误判为除颤节律，并实施不必要的电击，那么将会对病人的心脏产生极大的损伤，并导致严重的后果。因此为确保心室颤动的正确识别，在节律分析期间，必须停止对病人的胸外按压和通气过程。这一过程大约需要12至20秒的时间。在这一过程中，电击除颤的成功率因为室颤时间的延长而大大降低，尤其是在院外病人的复苏期间。因此如果能有一种比较可靠的心室颤动节律识别算法，即使是在对病人实施胸外按压期间也能对心电波形进行可靠分析，那么病人存活的几率将会得到有效提高。
(2)胸外心脏按压的有效性监测分析。胸外心脏按压的质量也是成功复苏的关键，包括按压深度、按压频率和胸廓的回弹程度。尤其的恰当的按压深度，它是保持一定冠状动脉灌注压(coronary perfusion pressure，CPP)的关键。但是，研究表明许多心脏骤停患者在心肺复苏过程中没有得到有效的胸外按压，主要表现在按压频率较低、按压深度不足以及没有保持适当的循环血流。而在院外急救过程中，胸外按压由于没有得到有效的监测，整个过程就只能靠抢救者的感觉和视觉判断。随着复苏过程的进行，急救人员急需了解胸外按压的效果以及由此产生的病人心脏血流的变化，从而实施进一步的治疗，包括优化电击除颤或继续胸外按压治疗。
(3)实时呼吸及脉搏检测。在过去的20年里，心室颤动或室性心动过速在心脏骤停中出现的比例逐渐下降，已经低于50％，而无脉搏心电活动PEA及其它类型的心脏骤停所占比例并没有改变。心室颤动的下降通过心室静止增加得以补偿。这就要求第一目击者或急救人员快速判断病人在失去意识的情况下，是否具有呼吸、脉搏或者足够的血液循环。但这两项指标的检测对院外急救来说却非常困难。因为传统脉搏检测是通过感触病人颈动脉的搏动来实现的，呼吸检测则是通过贴近病人嘴部感觉呼吸气流和观察胸腔的变化来实现。这些适用于普通人群的方法，很难应用于心跳和呼吸极其微弱的冠心病患者。若不能准确地检测呼吸与脉搏，就不能正确区分由于节律失常和窒息引起的心脏停搏，并实施正确的复苏措施。
针对以上心肺复苏过程中的问题，我们扩展了目前AED使用的心电采集及除颤电极功能，利用一对胸前除颤电极实现心电信号与胸阻抗信号的采集分析。通过对按压过程中心电信号的分析实现对胸外按压效果的监测，通过对胸阻抗信号的处理实现微弱呼吸与脉搏信号的检测，实现了心肺复苏的自动化实现方案，其中的主要算法包括：
(1)不间断胸外按压过程中的心电节律识别算法。采用基于连续小波变换及形态一致性评估的分析方法实现对心电信号的节律识别。通过对心电信号中R波形态一致性的量化分析，可以区别规则性心电节律(organized rhythm)和不规则心电节律(disorganized rhythm)。对于规则性心电节律，通过连续小波变换中R波峰值出现的频率来估计心率的变化，以区别室性心动过速与正常节律。而对于不规则性心电信号，则通过幅度频率谱面积分析来区分心室颤动与心室静止。
(2)胸外按压有效性的监测与电击除颤的优化算法。早期对胸外按压有效性的监测通过对心室颤动信号的幅度分析来实现。此后动物与临床实验研究表明，心室颤动信号的频率与CPP呈相关性。但由于心电信号的幅度与频率均因病人的个体差异而对实验结果有较大的影响。本研究小组将心电信号的幅度与频率相结合，提出了一种基于幅度频率谱面积的分析方法，它定义为一定频带宽度下信号功率谱所包含的面积。我们期望这种用于对电击除颤优化分析的方法可扩展应用于对胸外按压有效性的实时监测中。
(3)实时呼吸及脉搏检测分析算法。利用心电检测／除颤用电极提取的胸阻抗信号由两部分组成：一是包含了频率较低但幅度较高的呼吸阻抗信号，二是频率略高但幅度较低的反映心脏机械活动的心阻抗信号。我们用心电信号作为参考，利用自适应滤波器将呼吸阻抗信号和心阻抗信号相分离。最后用峰值检测算法利用检测到的心阻抗信号幅度，判断检测脉搏信号的有无，并用呼吸阻抗信号的幅度估计潮气量的大小，从而确定呼吸的类别。
为检验这些算法的有效性，我们对不同的算法进行了相应的实验设计与临床实验。对用AED记录的232例院外心脏骤停患者的心电信号分析结果表明，所提出的自动心电节律分析算法可以实现在胸外按压不间断情况下对心电节律的可靠分析。对除颤信号的检测敏感率为93％，特异性为89％。在一组由心律失常引起的心脏骤停动物模型中，幅度频率谱面积分析与CPP分析结果具有良好的相关性，并且对电击除颤结果的预测具有较高的准确性。在另一个由窒息引起的心脏骤停动物模型中，由心脏机械活动引起的心阻抗信号变化与脉搏电压具有良好的相关性，而由呼吸引起的呼吸阻抗信号的变化则与呼吸潮气量的变化正相关。
临床研究结果表明，通过对脉搏及呼吸信号的无创检测，本研究提出的算法能够正确地区分因心律失常和窒息引起的不同类型的心脏骤停，从而及时地提示急救人员实施相应的复苏方案。而在不间断胸外按压情况下对心电节律的实时分析，则有效地避免了因节律分析造成的延误，提高患者的存活率，同时可以避免不必要或不成功的电击除颤，较好地解决了当前心肺复苏过程中存在的不足，实现了心肺复苏的全自动化。
Cardiac arrest is a medical emergency with absent or inadequate contraction ofthe left ventricle of the heart that immediately causes bodywide circulatory failure. Atleast 225,000 people die in the United States every year from out-of-hospital suddencardiac arrest (CA) before they reach a hospital, and an estimated 370,000 to 750,000patients per year have a cardiac arrest and undergo cardiopulmonary resuscitation(CPR) during hospitalization. The overall incidence of cardiac arrest in China isestimated about 6,000,000 per year.
There are two different types of cardiac arrest: dysrhythmic cardiac arrest thatmost victims demonstrate ventricular fibrillation (VF) and asphyxial or respiratorycardiac arrest such as drowning, drug overdose or traumatic injuries, that bout5%～15% victims have VF. The primary cardiac arrest is emphasize on earlierdefibrillation and immediate CPR, while the best results for the resuscitation ofsecondary cardiac arrest is obtained by a combination of effective chest compressionsand ventilations.
Three major restraints are likely to account for currently poor outcomes, namely1) delays in starting cardiopulmonary resuscitation, including chest compression andexternal ventilation, 2) ineffective and interrupted chest compressions, and 3) limited access to, or delayed implementation of electrical defibrillation.
An important advance has been the capability for automated prompting of thelay rescuer through basic life support (BLS) maneuvers by newer versions ofautomated external defibrillators (AEDs). Current versions of AEDs have a sensitivityof 94% and a specificity approaching 100% for identifying shockable rhythms. Afterintroduction and expanded use of current versions of AEDs, multicenter trials haveindicated that the number of survivors may double or triple.
In most out-of-hospital situations, however, the patient is past the initial phaseof (Ventricular Tachycardia, VT) and is in the rhythm of VF, which is treated withdefibrillation. If no CPR is given, the electrical activity of the heart disappearsgradually and the final rhythm before death is termed asystole, which is a (nearly) flatline in the ECG tracing and is not treated with defibrillation. CPR and drugadministration may increase circulation of blood and oxygen to the ischemicmyocardium and might result in a conversion from asystole to VF, which then can bedefibrillated. During asystole and VF, the heart does not contract and these rhythmsare therefore pulseless. If no mechanical activity corresponds to an existing electricalactivity of the myocardium, the resulting rhythm is called "electromechanicaldissociation" (EMD) or "pulseless electrical activity" (PEA). This rhythm oftenappears in the transition from VF to asystole and is treated with medications and CPR,but does not respond to defibrillation attempts.
Presently, resuscitation is guided by a standardized protocol (the internationalguidelines) which includes cardiac rhythm, respiration and pulse detection fordecision support. But currently used AEDs can only perform automated rhythmanalysis. The respiration and pulse detection are depending on the rescuers. For theautomation of CPR, some key problems still need to be solved:
(1)Rhythm classification during uninterrupted chest compression. Current AEDs require repetitive analyses of ECG rhythms prior to prompting the delivery of anelectrical shock. Reliable analyses can only be achieved when both chest compressionand ventilation are discontinued for an interval of 12 seconds or more in addition tothe time that may be required for charging the capacitor of the defibrillator anddelivering a shock. With these interruptions, the lifesaving benefits of chestcompression are therefore seriously compromised. Interruption of chest compressionsis also documented as a major factor accounting for poor outcomes in victims ofcardiac arrest. Significantly better outcomes have therefore been reported if effectivechest compression precedes electrical defibrillation and especially if interruptionsmandated by a "hands off" interval are avoided.
(2)Monitoring the effectiveness of chest compression. The likelihood of returnof spontaneous circulation (ROSC) after more than 5 minutes of untreated VF isimproved when chest compressions precede the first defibrillation attempt. Therecognized role of chest compression is to restore the flow of oxygenated blood tovital organs, and thereby minimize ischemic injury, especially in the brain and in theheart. The quality of chest compressions relates to compression depth, compressionrate and the completeness of the recoil of the chest. Since chest compressions areusually performed without feedback and relatively small changes in the depth ofcompression profoundly alter hemodynamic effectiveness and outcomes, there is anincreasingly recognized need for such quality controls.
(3)Automated respiration and pulse detection. In the past 20 years, the incidenceof VF or VT during cardiac arrest has declined remarkably to less than 50%. Yet, theincidence of PEA and overall cardiac arrest remained unchanged. The decrease in theincidence of VF was accounted for by increases in the incidence of asystole. So it isnecessary for a person who is not a medical professional, to evaluate the condition ofa weak patient who has signs of cardiopulmonary arrest, especially when the patient is partly or completely unconscious. One of the first steps of a rescuer is to determineif there is sufficient breathing and adequate blood circulation. Unfortunately,evaluation of there two parameters for out-of-hospital patients is difficult. Foridentification of blood circulation by detecting a pulse, a common method is to placea hand on a region of the body (e. g. carotid artery) and feel for small fluctuations. Forevaluation of breathing, a common method is to place the rescuer's face near themouth to feel or hear the flow of air or movement of the chest. Although detection ofpulse and breathing is not difficult in normal people, such identification is difficult inweak patients, such as a patient in shock, where there is low flow of blood and of air.The inability to detect pulse and respiration at this time increases the probability of anincorrect diagnosis between cardiac arrest, breathing arrhythmia (irregular heartbeat),or asphyxia.
For the automation of CPR, we extended ECG characteristic analysis of themyocardium status of the victims for monitoring the effectiveness of chestcompression and thus to optimizing the time for defibrillation. For the automatedrespiration and pulse detection, we expanded the conventional precordial AEDsensing and defibrillation electrodes for transthoracic impedance measurement. Thealgorithms developed for automated CPR including:
1) Detecting a potential shockable rhythm during uninterrupted chestcompression. Wavelet based transformation and shape based morphology consistencydetection were utilized for rhythm classification. Morphological consistencies ofwaveform representing QRS components were analyzed to differentiate betweendisorganized and organized rhythms. For organized rhythms, the heart rate was thenestimated for further classification between shockable VT and non-shockable sinnsrhythms (SR). When disorganized rhythms were identified, the amplitude spectrumarea (AMSA) was computed in the frequency domain to then distinguish between shockable VF and non-shockable asystole.
2) Monitoring the effectiveness of chest compression and optimizing timing ofdefibrillation. Earlier investigations based on ECG recordings of VF obtained fromhuman victims of cardiac arrest focused on the amplitude of VF wavelets as apredictor of the likelihood of successful defibrillation. Subsequently, frequencyanalysis of VF wavelets and, specifically, median frequency was proposed as a moreprecise value which correlated with CPP in canine and porcine models and in humanvictims. The measurement was subsequently refined by us by combing amplitude andfrequency. This method of measurement evolved into the amplitude spectrum area(AMSA) which was defined by the area under the curve of amplitude and frequencyhas since been incorporated into commercially available AEDs. We were thereforeattracted to the possibility that AMSA derived from the conventional two precordialelectrodes, used for both sensing and defibrillation in conjunction with AEDs wouldserve as a monitor of effectiveness of chest compressions.
3) Automated respiration and pulse pressure detection. An algorithm used forrespiration and pulse pressure detection with the use of ECG and transthoracicimpedance signal that collected by the conventional electroacrdiographic sensing anddefibrillation electrodes applied for AEDs was proposed. By applying an adaptivefiltering method with the reference of heart rate derived from ECGs, the breathimpedance signal and cardiac impedance signal that represents the mechanical heartbeat are separately derived. The tidal volume and pulse pressure are then can beestimated from the breath impedance signal and cardiac impedance signal.
These algorithms are optimized and validated on human and animal data whichwere collected at the Weil Institute of Critical Care Medicine (Rancho Mirage, CA,U.S.A). The rhythm classification algorithm during chest compression yielded asensitivity of 93%, a specificity of 89% for detecting a shockable rhythm with dataset collected from out-of-hospital cardiac arrest victims by AEDs. In an animalexperiment of arrhythmia cardiac arrest model, the effectiveness of chestcompressions was reflected in the AMSA value as it previously was in CPP.Accordingly, the more routinely available electrocardiographic AMSA value mayserve to monitor the effectiveness of chest compression during CPR. In an animalexperiment of asphyxial cardiac arrest model, the change in the measured cardiacimpedance was linearly correlated with the pulse pressure (R~2=0.95). Likewise, themagnitude of the measured respiratory impedance signal was quantitatively related tothe tidal volume (R~2=0.91).
The research fulfilled the potential life saving advantages by differentiatebetween asphyxial cardiac arrest and dysrhythmic arrest and prompting accordinglywith respiration and pulse detection, and avoid the adverse effects of unsuccessfulelectrical shocks by rhythm analysis during uninterrupted chest compression andmonitoring the effectiveness of CPR.
由于心脏骤停常是冠心病的首发表现形式，有效的心肺复苏是抢救这类患者的唯一途径。心肺复苏是通过对心脏骤停的快速识别和积极抢救，人工重建或恢复自主呼吸与循环，避免发生心肺脑功能不全。快速采取基本生命支持(basic life support，BLS)是心肺复苏成功的关键。
心脏骤停有两种不同的形式。一种是由于节律失常引起的心脏骤停(dysarhithmic cardiac arrest)，另一种是因为呼吸停止引起的心脏骤停(asphyxial or respiratory cardiac arrest)。前一种类型的心脏骤停患者大都出现心室颤动(ventricular fibrillation，VF)，而后一类心脏骤停则是由于溺死、药物过量或外伤引起，只有约5％～15％的患者出现心室颤动。对前一类型的患者强调早期除颤和即时的心肺复苏，而对后一种类型的患者则需要实施有效的胸外按压和人工通气治疗。
目前心肺复苏有效的抢救成功率依然很低，只有对其不断改进才有可能降低死亡率，这主要是由于以下三个原因引起的。(1)心肺复苏开始得比较晚，包括胸外按压和人工通气。(2)胸外按压的效率较低以及频繁间断。(3)电击除颤不及时造成抢救时机的延误。
心室颤动如不及时去除可在数分钟内转为心室静止(asystole)。为了增加早期除颤的机会，尽量缩短除颤时间，体外自动除颤仪(automatic external defibrillator, AED)应运而生。AED的最大特点是提高了电击除颤的自动化程度，是专为非医务人员和初级救生员设计使用的，其识别心室颤动的敏感性与特异性均超过94％。抢救人员只要发现患者意识丧失，无脉搏就可将AED置于患者的胸壁上并启动开关，AED感知心电信号，如能识别出室性心动过速(Ventricular Tachycardia，VT)，或心室颤动，就可自动除颤。应用AED后的研究显示，与单纯的基础生命支持相比可明显提高存活率。美国AHA和IAFC已要求每辆急救车和消防车均需配备AED。随着AED的推广和普及，可以期望更多的生命将会被挽救。
由于心脏骤停患者绝大部分(60％-70％)发生在院前，而且在常温下心脏停搏5分钟后脑细胞即可发生不可逆损害，10分钟后脑细胞死亡。在此期间如果不实施心肺复苏术，则心脏的电活动就会逐渐消失，最后出现心室静止，心电图出现一条直线。心肺复苏和药物治疗可能会增加缺血心肌的血液和氧气循环，从而将心室静止转变为心室颤动，而后方可电击除颤。因心室静止和心室颤动期间心脏停止收缩，因此无法检测到脉搏信号。如果心脏只存在心肌的电活动而没有相应的机械收缩，则称为心电机械分离(electromechanical dissociation，EMD)或无脉搏心电活动(pulseless electrical activity, PEA)。这种情况常会出现在药物治疗或心肺复苏但没有实施电击除颤，或由心室颤动转变为心室静止的过程中。
目前心肺复苏有一个标准的操作指南(the international guidelines)，它根据对呼吸、脉搏以及心电节律的检测来确定相应的复苏措施。而目前使用的AED只能根据患者的心电波形做出相应的节律分析决定是否需要电击除颤，其它如呼吸检测、脉搏检查等均需要由目击者或抢救人员来判断。为实现心肺复苏的全自动化，目前仍需要解决以下一些问题：
(1)胸外按压过程中的心电节律识别。心脏骤停患者的存活率随心室颤动持续时间的延长而迅速降低，平均每分钟下降约7％～10％。当施行电击除颤的时间延迟10～12分钟以上时，存活的可能性几乎为零。尽早应用基础的心肺复苏，并尽快实施电击除颤，可有效提高心脏骤停患者的存活率。但目前使用的体外自动除颤仪在实施电击除颤之前需要反复进行节律分析。否则如果将正常的非除颤心室搏动节律误判为除颤节律，并实施不必要的电击，那么将会对病人的心脏产生极大的损伤，并导致严重的后果。因此为确保心室颤动的正确识别，在节律分析期间，必须停止对病人的胸外按压和通气过程。这一过程大约需要12至20秒的时间。在这一过程中，电击除颤的成功率因为室颤时间的延长而大大降低，尤其是在院外病人的复苏期间。因此如果能有一种比较可靠的心室颤动节律识别算法，即使是在对病人实施胸外按压期间也能对心电波形进行可靠分析，那么病人存活的几率将会得到有效提高。
(2)胸外心脏按压的有效性监测分析。胸外心脏按压的质量也是成功复苏的关键，包括按压深度、按压频率和胸廓的回弹程度。尤其的恰当的按压深度，它是保持一定冠状动脉灌注压(coronary perfusion pressure，CPP)的关键。但是，研究表明许多心脏骤停患者在心肺复苏过程中没有得到有效的胸外按压，主要表现在按压频率较低、按压深度不足以及没有保持适当的循环血流。而在院外急救过程中，胸外按压由于没有得到有效的监测，整个过程就只能靠抢救者的感觉和视觉判断。随着复苏过程的进行，急救人员急需了解胸外按压的效果以及由此产生的病人心脏血流的变化，从而实施进一步的治疗，包括优化电击除颤或继续胸外按压治疗。
(3)实时呼吸及脉搏检测。在过去的20年里，心室颤动或室性心动过速在心脏骤停中出现的比例逐渐下降，已经低于50％，而无脉搏心电活动PEA及其它类型的心脏骤停所占比例并没有改变。心室颤动的下降通过心室静止增加得以补偿。这就要求第一目击者或急救人员快速判断病人在失去意识的情况下，是否具有呼吸、脉搏或者足够的血液循环。但这两项指标的检测对院外急救来说却非常困难。因为传统脉搏检测是通过感触病人颈动脉的搏动来实现的，呼吸检测则是通过贴近病人嘴部感觉呼吸气流和观察胸腔的变化来实现。这些适用于普通人群的方法，很难应用于心跳和呼吸极其微弱的冠心病患者。若不能准确地检测呼吸与脉搏，就不能正确区分由于节律失常和窒息引起的心脏停搏，并实施正确的复苏措施。
针对以上心肺复苏过程中的问题，我们扩展了目前AED使用的心电采集及除颤电极功能，利用一对胸前除颤电极实现心电信号与胸阻抗信号的采集分析。通过对按压过程中心电信号的分析实现对胸外按压效果的监测，通过对胸阻抗信号的处理实现微弱呼吸与脉搏信号的检测，实现了心肺复苏的自动化实现方案，其中的主要算法包括：
(1)不间断胸外按压过程中的心电节律识别算法。采用基于连续小波变换及形态一致性评估的分析方法实现对心电信号的节律识别。通过对心电信号中R波形态一致性的量化分析，可以区别规则性心电节律(organized rhythm)和不规则心电节律(disorganized rhythm)。对于规则性心电节律，通过连续小波变换中R波峰值出现的频率来估计心率的变化，以区别室性心动过速与正常节律。而对于不规则性心电信号，则通过幅度频率谱面积分析来区分心室颤动与心室静止。
(2)胸外按压有效性的监测与电击除颤的优化算法。早期对胸外按压有效性的监测通过对心室颤动信号的幅度分析来实现。此后动物与临床实验研究表明，心室颤动信号的频率与CPP呈相关性。但由于心电信号的幅度与频率均因病人的个体差异而对实验结果有较大的影响。本研究小组将心电信号的幅度与频率相结合，提出了一种基于幅度频率谱面积的分析方法，它定义为一定频带宽度下信号功率谱所包含的面积。我们期望这种用于对电击除颤优化分析的方法可扩展应用于对胸外按压有效性的实时监测中。
(3)实时呼吸及脉搏检测分析算法。利用心电检测／除颤用电极提取的胸阻抗信号由两部分组成：一是包含了频率较低但幅度较高的呼吸阻抗信号，二是频率略高但幅度较低的反映心脏机械活动的心阻抗信号。我们用心电信号作为参考，利用自适应滤波器将呼吸阻抗信号和心阻抗信号相分离。最后用峰值检测算法利用检测到的心阻抗信号幅度，判断检测脉搏信号的有无，并用呼吸阻抗信号的幅度估计潮气量的大小，从而确定呼吸的类别。
为检验这些算法的有效性，我们对不同的算法进行了相应的实验设计与临床实验。对用AED记录的232例院外心脏骤停患者的心电信号分析结果表明，所提出的自动心电节律分析算法可以实现在胸外按压不间断情况下对心电节律的可靠分析。对除颤信号的检测敏感率为93％，特异性为89％。在一组由心律失常引起的心脏骤停动物模型中，幅度频率谱面积分析与CPP分析结果具有良好的相关性，并且对电击除颤结果的预测具有较高的准确性。在另一个由窒息引起的心脏骤停动物模型中，由心脏机械活动引起的心阻抗信号变化与脉搏电压具有良好的相关性，而由呼吸引起的呼吸阻抗信号的变化则与呼吸潮气量的变化正相关。
临床研究结果表明，通过对脉搏及呼吸信号的无创检测，本研究提出的算法能够正确地区分因心律失常和窒息引起的不同类型的心脏骤停，从而及时地提示急救人员实施相应的复苏方案。而在不间断胸外按压情况下对心电节律的实时分析，则有效地避免了因节律分析造成的延误，提高患者的存活率，同时可以避免不必要或不成功的电击除颤，较好地解决了当前心肺复苏过程中存在的不足，实现了心肺复苏的全自动化。
Cardiac arrest is a medical emergency with absent or inadequate contraction ofthe left ventricle of the heart that immediately causes bodywide circulatory failure. Atleast 225,000 people die in the United States every year from out-of-hospital suddencardiac arrest (CA) before they reach a hospital, and an estimated 370,000 to 750,000patients per year have a cardiac arrest and undergo cardiopulmonary resuscitation(CPR) during hospitalization. The overall incidence of cardiac arrest in China isestimated about 6,000,000 per year.
There are two different types of cardiac arrest: dysrhythmic cardiac arrest thatmost victims demonstrate ventricular fibrillation (VF) and asphyxial or respiratorycardiac arrest such as drowning, drug overdose or traumatic injuries, that bout5%～15% victims have VF. The primary cardiac arrest is emphasize on earlierdefibrillation and immediate CPR, while the best results for the resuscitation ofsecondary cardiac arrest is obtained by a combination of effective chest compressionsand ventilations.
Three major restraints are likely to account for currently poor outcomes, namely1) delays in starting cardiopulmonary resuscitation, including chest compression andexternal ventilation, 2) ineffective and interrupted chest compressions, and 3) limited access to, or delayed implementation of electrical defibrillation.
An important advance has been the capability for automated prompting of thelay rescuer through basic life support (BLS) maneuvers by newer versions ofautomated external defibrillators (AEDs). Current versions of AEDs have a sensitivityof 94% and a specificity approaching 100% for identifying shockable rhythms. Afterintroduction and expanded use of current versions of AEDs, multicenter trials haveindicated that the number of survivors may double or triple.
In most out-of-hospital situations, however, the patient is past the initial phaseof (Ventricular Tachycardia, VT) and is in the rhythm of VF, which is treated withdefibrillation. If no CPR is given, the electrical activity of the heart disappearsgradually and the final rhythm before death is termed asystole, which is a (nearly) flatline in the ECG tracing and is not treated with defibrillation. CPR and drugadministration may increase circulation of blood and oxygen to the ischemicmyocardium and might result in a conversion from asystole to VF, which then can bedefibrillated. During asystole and VF, the heart does not contract and these rhythmsare therefore pulseless. If no mechanical activity corresponds to an existing electricalactivity of the myocardium, the resulting rhythm is called "electromechanicaldissociation" (EMD) or "pulseless electrical activity" (PEA). This rhythm oftenappears in the transition from VF to asystole and is treated with medications and CPR,but does not respond to defibrillation attempts.
Presently, resuscitation is guided by a standardized protocol (the internationalguidelines) which includes cardiac rhythm, respiration and pulse detection fordecision support. But currently used AEDs can only perform automated rhythmanalysis. The respiration and pulse detection are depending on the rescuers. For theautomation of CPR, some key problems still need to be solved:
(1)Rhythm classification during uninterrupted chest compression. Current AEDs require repetitive analyses of ECG rhythms prior to prompting the delivery of anelectrical shock. Reliable analyses can only be achieved when both chest compressionand ventilation are discontinued for an interval of 12 seconds or more in addition tothe time that may be required for charging the capacitor of the defibrillator anddelivering a shock. With these interruptions, the lifesaving benefits of chestcompression are therefore seriously compromised. Interruption of chest compressionsis also documented as a major factor accounting for poor outcomes in victims ofcardiac arrest. Significantly better outcomes have therefore been reported if effectivechest compression precedes electrical defibrillation and especially if interruptionsmandated by a "hands off" interval are avoided.
(2)Monitoring the effectiveness of chest compression. The likelihood of returnof spontaneous circulation (ROSC) after more than 5 minutes of untreated VF isimproved when chest compressions precede the first defibrillation attempt. Therecognized role of chest compression is to restore the flow of oxygenated blood tovital organs, and thereby minimize ischemic injury, especially in the brain and in theheart. The quality of chest compressions relates to compression depth, compressionrate and the completeness of the recoil of the chest. Since chest compressions areusually performed without feedback and relatively small changes in the depth ofcompression profoundly alter hemodynamic effectiveness and outcomes, there is anincreasingly recognized need for such quality controls.
(3)Automated respiration and pulse detection. In the past 20 years, the incidenceof VF or VT during cardiac arrest has declined remarkably to less than 50%. Yet, theincidence of PEA and overall cardiac arrest remained unchanged. The decrease in theincidence of VF was accounted for by increases in the incidence of asystole. So it isnecessary for a person who is not a medical professional, to evaluate the condition ofa weak patient who has signs of cardiopulmonary arrest, especially when the patient is partly or completely unconscious. One of the first steps of a rescuer is to determineif there is sufficient breathing and adequate blood circulation. Unfortunately,evaluation of there two parameters for out-of-hospital patients is difficult. Foridentification of blood circulation by detecting a pulse, a common method is to placea hand on a region of the body (e. g. carotid artery) and feel for small fluctuations. Forevaluation of breathing, a common method is to place the rescuer's face near themouth to feel or hear the flow of air or movement of the chest. Although detection ofpulse and breathing is not difficult in normal people, such identification is difficult inweak patients, such as a patient in shock, where there is low flow of blood and of air.The inability to detect pulse and respiration at this time increases the probability of anincorrect diagnosis between cardiac arrest, breathing arrhythmia (irregular heartbeat),or asphyxia.
For the automation of CPR, we extended ECG characteristic analysis of themyocardium status of the victims for monitoring the effectiveness of chestcompression and thus to optimizing the time for defibrillation. For the automatedrespiration and pulse detection, we expanded the conventional precordial AEDsensing and defibrillation electrodes for transthoracic impedance measurement. Thealgorithms developed for automated CPR including:
1) Detecting a potential shockable rhythm during uninterrupted chestcompression. Wavelet based transformation and shape based morphology consistencydetection were utilized for rhythm classification. Morphological consistencies ofwaveform representing QRS components were analyzed to differentiate betweendisorganized and organized rhythms. For organized rhythms, the heart rate was thenestimated for further classification between shockable VT and non-shockable sinnsrhythms (SR). When disorganized rhythms were identified, the amplitude spectrumarea (AMSA) was computed in the frequency domain to then distinguish between shockable VF and non-shockable asystole.
2) Monitoring the effectiveness of chest compression and optimizing timing ofdefibrillation. Earlier investigations based on ECG recordings of VF obtained fromhuman victims of cardiac arrest focused on the amplitude of VF wavelets as apredictor of the likelihood of successful defibrillation. Subsequently, frequencyanalysis of VF wavelets and, specifically, median frequency was proposed as a moreprecise value which correlated with CPP in canine and porcine models and in humanvictims. The measurement was subsequently refined by us by combing amplitude andfrequency. This method of measurement evolved into the amplitude spectrum area(AMSA) which was defined by the area under the curve of amplitude and frequencyhas since been incorporated into commercially available AEDs. We were thereforeattracted to the possibility that AMSA derived from the conventional two precordialelectrodes, used for both sensing and defibrillation in conjunction with AEDs wouldserve as a monitor of effectiveness of chest compressions.
3) Automated respiration and pulse pressure detection. An algorithm used forrespiration and pulse pressure detection with the use of ECG and transthoracicimpedance signal that collected by the conventional electroacrdiographic sensing anddefibrillation electrodes applied for AEDs was proposed. By applying an adaptivefiltering method with the reference of heart rate derived from ECGs, the breathimpedance signal and cardiac impedance signal that represents the mechanical heartbeat are separately derived. The tidal volume and pulse pressure are then can beestimated from the breath impedance signal and cardiac impedance signal.
These algorithms are optimized and validated on human and animal data whichwere collected at the Weil Institute of Critical Care Medicine (Rancho Mirage, CA,U.S.A). The rhythm classification algorithm during chest compression yielded asensitivity of 93%, a specificity of 89% for detecting a shockable rhythm with dataset collected from out-of-hospital cardiac arrest victims by AEDs. In an animalexperiment of arrhythmia cardiac arrest model, the effectiveness of chestcompressions was reflected in the AMSA value as it previously was in CPP.Accordingly, the more routinely available electrocardiographic AMSA value mayserve to monitor the effectiveness of chest compression during CPR. In an animalexperiment of asphyxial cardiac arrest model, the change in the measured cardiacimpedance was linearly correlated with the pulse pressure (R~2=0.95). Likewise, themagnitude of the measured respiratory impedance signal was quantitatively related tothe tidal volume (R~2=0.91).
The research fulfilled the potential life saving advantages by differentiatebetween asphyxial cardiac arrest and dysrhythmic arrest and prompting accordinglywith respiration and pulse detection, and avoid the adverse effects of unsuccessfulelectrical shocks by rhythm analysis during uninterrupted chest compression andmonitoring the effectiveness of CPR.
引文
[1] Richard OC, Douglas C, Mary FH et al. Recommended guidelines for reviewing, reporting and conducting research on in-hospital resuscitation. Circulation, 1997,95: 2213-2239.
[2] Langhelle A, Nolan J, Herlitz J et al. 2003 Utstein Consensus Symposium. Recommended guidelines for reviewing, reporting, and conducting research on post-resuscitation care: the Utstein style. Resuscitation, 2005, 66(3):271-83.
[3] Culley LL, Rea TD, Murray JA et al. Public access defibrillation in out-of-hospital cardiac arrest: a community-based study. Circulation, 2004, 109:1859-63.
[4] Chugh SS, Jui J, Gunson K et al. Current burden of sudden cardiac death: multiple source surveillance versus retrospective death certificate- based review in a large US community. J Am Coll Cardiol, 2004, 44:1268 -1275.
[5] Vaillancourt C, Stiell IG. Cardiac arrest care and emergency medical services in Canada. Can J Cardiol, 2004,20:1081-1090.
[6] Eisenberg MS, Mengert TJ. Cardiac resuscitation. N Engl J Med, 2001, 344(17): 1304-13.
[7] Herlitz J, Bahr J, Fischer M et al. Resuscitation in Europe: a tale of five European regions. Resuscitation, 1999, 41(2):121-31.
[8] Becker LB. Epidemiology: Etiology, Incidence, and Survival Rates. In M.H. Weil, W. Tang, and A. Ross, editors, CPR: Resuscitation of the arrested heart, pages 13-27. W.B. Saunders Company, Philadelphia, 1999.
[9] Becker LB, Ostrander MP, Barrett J et al. Outcome of CPR in a large metropolitan area-where are the survivors? Ann Emerg Med, 1991, 20(4):355-61.
[10] Lombardi G, Gallagher J, and Gennis P. Outcome of out-of-hospital cardiac arrest in New York City. The Pre-Hospital Arrest Survival Evaluation (PHASE) Study. JAMA, 1994, 271(9):678-83.
[11] Weil MH, Tang W, Bisera J. Cardiopulmonary resuscitation: one size does not fit all. Circulation, 2003, 107(6):794.
[12] Jin X, Pernat A, Bisera J. Electrical Causes of Cardiac Arrest and Their Managment. In M.H. Weil, W. Tang, and A. Ross, editors, CPR: Resuscitation of the arrested heart., pages 37-51. W.B. Saunders Company, Philadelphia, 1999.
[13] Tang W, Weil MH, Sun S et al. The effects of biphasic and conventional monophasic defibrillation on postresuscitation myocardial function. J Am Coll Cardiol, 1999, 34(3):815-22.
[14] Xie J, Weil MH, Sun S et al. High-energy defibrillation increases the severity of postresuscitation myocardial dysfunction. Circulation, 1997, 96(2):683-8.
[15] Deshmukh HG, Weil MH, Gudipati CV et al. Mechanism of blood flow generated by precordial compression during CPR. I. Studies on closed chest precordial compression. Chest, 1989, 95(5): 1092-9.
[16] Larsen MP, Eisenberg MS, Cummins RO et al. Predicting survival from out-of-hospital cardiac arrest: a graphic model. Ann Emerg Med, 1993, 22:1652-1658.
[17] Valenzuela TD, Roe DJ, Cretin S et al. Estimating effectiveness of cardiac arrest interventions: a logistic regression survival model. Circulation, 1997, 96:3308-3313.
[18] Holmberg M, Holmberg S, Herlitz J. Factors modifying the effect of bystander cardiopulmonary resuscitation on survival in out-of-hospital cardiac arrest patients in Sweden. Eur Heart J, 2001, 22: 511-519.
[19] Holmberg M, Holmberg S, Herlitz J et al. Survival after cardiac arrest outside hospital in Sweden. Swedish Cardiac Arrest Registry. Resuscitation, 1998, 36:29 -36.
[20] Cobb LA, Fahrenbruch CE, Walsh TR et al. Influence of cardiopulmonary resuscitation prior to defibrillation in patients with out-of-hospital ventricular fibrillation. JAMA, 1999,281:1182-1188.
[21] Wik L, Hansen TB, Fylling F et al. Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial. JAMA, 2003, 289:1389 -1395.
[22] White RD, Russell JK. Refibrillation, resuscitation and survival in out-of-hospital sudden cardiac arrest victims treated with biphasic automated external defibrillators. Resuscitation, 2002, 55:17-23.
[23] Carpenter J, Rea TD, Murray JA, et al. Defibrillation waveform and post-shock rhythm in out-of-hospital ventricular fibrillation cardiac arrest. Resuscitation, 2003,59:189-196.
[24] Berg MD, Clark LL, Valenzuela TD et al. Post-shock chest compression delays with automated external defibrillator use. Resucitation,2005,64:287-291.
[25] Appleton GO, Cummins RO, Larson MP et al. CPR and the single rescuer: at what age should you "call first" rather than "call fast"? Ann Emerg Med, 1995, 25:492-494.
[26] Hickey RW, Cohen DM, Strausbaugh S et al. Pediatric patients requiring CPR in the prehospital setting. Ann Emerg Med, 1995, 25:495-501.
[27] Mogayzel C, Quan L, Graves JR et al. Out-of-hospital ventricular fibrillation in children and adolescents: causes and outcomes. Ann Emerg Med, 1995, 25:484-491.
[28] Berg RA, Hilwig RW, Kern KB et al. Simulated mouth-to-mouth ventilation and chest compressions (bystander cardiopulmonary resuscitation) improves outcome in a swine model of prehospital pediatric asphyxial cardiac arrest. Crit Care Med, 1999,27:1893-1899.
[29] Berg RA, Hilwig RW, Kern KB et al. "Bystander" chest compressions and assisted ventilation independently improve outcome from piglet asphyxial pulseless "cardiac arrest." Circulation, 2000, 101:1743-1748.
[30] 2005 American Heart Association Guidelines for Cardiopulmonary Resuscitatiuon and Emergency Cardiovascular Care. Circulation, 2005, 112 [Suppl I]:IV-19-IV-34.
[31] 2005 American Heart Association Guidelines for Cardiopulmonary Resuscitatiuon and Emergency Cardiovascular Care. Circulation, 2005, 112 [Suppl I]: IV-58 - IV-66.
[32] K.B. Kern and J.T. Niemann. Coronary perfusion pressure during cardiopulmonary resuscitation. In N.A. Paradis, H.R. Halperin, and R.M. Nowak, editors, Cardiac arrest: The science and practice of resuscitation medicine. chapter 14 A, pages 270-285. Williams & Wilkins, Baltimore, 1996.
[33] Kern KB, Ewy GA, Voorhees WD et al. Myocardial perfusion pressure: a predictor of 24-hour survival during prolonged cardiac arrest in dogs. Resuscitation, 1988,16(4):241-50.
[34] Paradis NA, Martin GB, Rivers EP et al. Coronary perfusion pressure and the return of spontaneous circulation in human cardiopulmonary resuscitation. JAMA, 1990, 263(8): 1106-13.
[35] Weil MH, Bisera J, Trevino RP et al. Cardiac output and end-tidal carbon dioxide. Crit Care Med, 1985,13(11):907-9.
[36] Reed MJ, Clegg GR, Robertson CE. Analysing the ventricular fibrillation waveform. Resuscitation, 2003, 57(1):11-20.
[37] Yu T, Weil MH, Tang W et al. Adverse outcomes of interrupted precordial compression during automated defibrillation. Circulation, 2002,106(3):368-72.
[38] Eftestol T, Sunde K, Steen PA. Effects of interrupting precordial compressions on the calculated probability of defibrillation success during out-of-hospital cardiac arrest. Circulation, 2002, 105:2270-3.
[39] Cobb LA, Fahrenbruch CE, Walsh TR et al. Influence of CPR prior to defibrillation in patients with out-of-hospital ventricular fibrillation. JAMA, 1999,281(13): 1182-8.
[40] Wik L, Hansen TB, Fylling F et al. Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial. JAMA, 2003,289(11):1389-95.
[41] Kern KB, Hilwig RW, Berg RA et al. Importance of continuous chest compressions during CPR: improved outcome during a simulated single lay-rescuer scenario. Circulation, 2002,105(5):645-9.
[42] Wik L, Steen PA, Bircher NG. Quality of bystander cardiopulmonary resuscitation influences outcome after prehospital cardiac arrest. Resuscitation, 1994,28:195-203.
[43] Gallagher EJ, Lombardi G, Gennis P. Effectiveness of bystander cardiopulmonary resuscitation and survival following out-of-hospital cardiac arrest. JAMA, 1995, 274: 1922-1925
[44] Van Hoeyweghen EJ, Bossaret LL, Mullie A et al. Quality and efficiency of bystander CPR. Belgian Cerebral Resuscitation Study Group. Resuscitation, 1993, 26: 47-52
[45] Brown TB, Dias JA, Saini D et al. Relationship between knowledge of cardiopulmonary resuscitation guidelines and performance. Resuscitation, 2006, 69(2): 253-61.
[46] Abella BS, Sandbo N, Alvarado JP et al. Quality of cardiopulmonary resuscitation during in-hospital cardiac arrest. JAMA, 2005, 293: 305-10.
[47] Wik L, Kramer-Johansen J, Myklebust H et al. Quality of cardiopulmonary resuscitation during out-of-hospital cardiac arrest. JAMA, 2005, 293: 299-304.
[48] Baubin M, Haid C, Hamm P et al. Measuring forces and frequency during active compression decompression cardiopulmonary resuscitation: a device for training, research and real CPR. Resuscitation, 1999, 43(1): 17-24.
[49] Aase SO, Myklebust H. Compression depth estimation for CPR quality assessment using DSP on accelerometer signals. IEEE Trans Biomed Eng, 2002, 49(3):263-8.
[50] Eftestol T, Sunde K, Steen PA. The effects of interrupting precordial compressions on the calculated probability of defibrillation success during out-of-hospital cardiac arrest. Circulation, 2002,105:2270-3.
[51] Eftestol T, Sunde K, Ole Aase S et al. Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest. Circulation, 2000, 26;102(13):1523-9.
[52] Watson JN, Uchaipichat N, Addison PS et al. Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods.Resuscitation, 2004, 63(3):269-75.
[53] Marn-Pernat A, Weil MH, Tang W et al. Optimizing timing of ventricular defibrillation. Crit Care Med, 2001, 29(12):2360-5.
[54] Menegazzi JJ, Callaway CW, Sherman LD et al. Ventricular fibrillation scaling exponent can guide timing of defibrillation and other therapies. Circulation. 2004 Feb 24;109(7):926-31.
55. Sedgwick ML, Watson J, Dalziel K, Carrington DJ, Cobbe SM. Efficacy of out of hospital defibrillation by ambulance technicians using automated external defibrillators. The Heartstart Scotland Project. Resuscitation, 1992, 24(1):73-87.
[56] Clifford AC. Comparative assessment of shockable ECG rhythm detection algorithms in automated external defibrillators. Resuscitation, 1996, 32(3):217-25.
[57] Thakor NV, Sheng ZY, and Yan PK. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE. Trans. Biomed. Eng, 1990,37(9), 837-843.
[58] Chen SW, Clarkson PM, and Fan Q. A robust sequential detection algorithm for cardiac arrhythmias classification. IEEE. Trans. Biomed. Eng., 1996, 43, no. 11, 1120-1125.
[59] Lin D, Dicarlo LJ, and Jenkins JM. Identification of ventricular tachycardia using interactivity ventricular electrograms analysis of time and frequency domain patterns. PACE, 1988, 11, 1592-1606.
[60] Clayton R, Murray A, and Campell R. Changes in the surface ECG frequency spectrum during the onset of ventricular fibrillation. in Proceedings on Computers in Cardiology. Los Alamos, CA: IEEE Computer Society Press, 1991,515-518.
[61] Afono VX and Becker WJ. Detecting ventricular fibrillation, Selecting the appropriate time frequency analysis tool for the application. Inst. Elect. Eng, 1995, 152-159.
[62] Khadra L, Al-Fahoum AS, and Al-Nashash H. Detection of life-threatening cardiac arrhythmiass using wavelet transformation. Inst. Elect. Eng. Med. Biol. Eng. Comput, 1997, 35, pp. 626-632.
[63] Lawandowski P, Meste O, Maniewski R et al. Risk evaluation of ventricular tachycardia using wavelet transform irregularity of the high-resolution electrocardiogram. Inst. Elect. Eng. Med. Biol. Eng. Comput, 2000, 38, 666-673.
[64] Zhang XS, Zhu YS, Thakor NV et al. Detecing ventricular tachycardia and fibrillation by complexity measure. IEEE. Trans. Biomed. Eng, 1999, 46(5):548-555.
[65] Felice MR, Richard JP, andKristina MR. Identification of ECG Arrhythmiass Using Phase Space Reconstruction. PKDD, 2001, 2168:411-423.
[66] Paul SA.Wavelet transforms and the ECG: a review. Physiol. Meas, 2005, 26, pp. 188-199.
[67] Romero L, Addison PS, and Grubb N. R-wave detection using continuous wavelet modulus maxima. Computers in Cardiology, 2003, 30: 565-568.
[68] Shubha K, Robin M and Faye GB. Wavelet transform-based QRS Complex detector. IEEE transactions on biomedical engineering, 1999, 46: 838-848.
[69] Fitzgibbon E, Berger R, Tsitlik J et al. Determination of the noise source in the electrocardiogram during cardiopulmonary resuscitation. Crit Care Med, 2002, 30: 148-153.
[70] Langhelle A, Eftestol T, Myklebust H et al. Reducing CPR artifacts in ventricular fibrillation in vitro. Resuscitation, 2001, 48:279-291.
[71] Watson JN, Addison PS, Clegg GR et al. A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation. Resuscitation, 2000, 43:121-127.
[72] Strohmenger HU, Aalen O, and Brown CG. Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans. Chest, 1997,111:1584-589.
[73] Aase SO, Eftestol T, Husoy HJ et al. CPR artifact removal from human ECG using optimal multichannel filtering. IEEE Trans Biomed Engr, 2000, 47: 1440-1449.
[74] Husoy HJ, Eilevstjonn J, Eftestol T et al. Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm. IEEE Trans Biomed Eng, 2002,49:1287-1298.
[75] Grossmann A. Wavelet transforms and edge detection. In S. A. et al., editor, Stochastic Processes in Physics and Engineering, 149—157. D. Reidel Publishing Company, 1988.
[76] Li C, Zheng C, and Tai C. Detection of ECG characteristic points using wavelet transforms. 1995, 42: 21-28.
[77] Daubechies I. Ten lectures on wavelets. CBMS, SIAM, 1994, 61:194-202.
[78] Zoran N and Joel WB. Spike detection using the continuous wavelet transform. IEEE transactions on biomedical engineering, 2005, 52(1): 74-87.
[79] Malletand S and Hwang WL. Singularity detetion and processing with wavelets. IEEE Trans. Inform. Theory, 1992, 38(2):617-643.
[80] Bruce LM and Adhami RR. Classifying mammographic mass shapes using the wavelet transform modulus maxima method. IEEE Trans. Med. Imaging, 1999, 18:1170-7.
[81] Povoas HP, Bisera J. Electrocardiographic waveform analysis for predicting the success of defibrillation. Crit Care Med, 2000, 28: 210-211.
[82] Marn-Pernat S, Weil MH, Tang W et al. Optimizing timing of ventricular defibrillation. Crit Care Med, 2001, 29: 2360-2365.
[83] Young C, Bisera J, Gehman S et al. Amplitude spectrum area: Measuring the probability of successful defibrillation as applied to human data. Crit Care Med, 2004, 32:356-358.
[84] Kuo S and Dillman R. Computer detection of ventricular fibrillation. Computers in Cardiology, IEEE Computer Society, 1978, 347-349.
[85] Barro S, Ruiz R, Cabello D et al. Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmiass and imitative artifacts: a diagnostic system. Journ. Biomed. Eng, 1989,11:320-328.
[86] Jekova I. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. Physiol. Meas, 2000,21:429-439.
[87] Anton A, Robert T, and Kal U. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. BioMedical Engineering OnLine, 2005,4:60 doi:10.1186/1475-925X-4-60.
[88] Noordergraaf GJ, Drinkwaard BW, Van Berkom PF et al. The quality of chest compressions by trained personnel: the effectof feedback, via the CPREzy, in a randomized controlled trial using a manikin model. Resuscitation, 2006, 69:241-52.
[89] Kette F, Weil MH, Gazmuri RJ. Buffer solutions may compromise cardiac resuscitation by reducing coronary perfusion pressure. JAMA, 1991, 266: 2121-2126.
[90] Tang W, Weil MH, Sun S, Gazmuri RJ, Bisera J. Progressive myocardial dysfunction after cardiac resuscitation. Crit Care Med 1993;21:1046-1050.
[91] Tang W, Weil MH, Sun RJ et al. The effects of biphasic and conventional monophasic defibrillation on post resuscitation myocardial function. J Am Coll Cardiol, 1999,4:815-822.
[92] Nieman JT, Criley JM, Rosborough JP et al. Predictive indices of successful cardiac resuscitation after prolonged arrest and experimental cardiopulmonary resuscitation. Ann Emerg Med, 1985,14:521-528.
[93] Gudipati CV, Weil MH, Bisera J et al. Expired carbon dioxide: a noninvasive monitor of cardiopulmonary resuscitation. Circulation, 1988, 77(l):234-9.
[94] Idris AH, Staples ED, O'Brien DJ et al. Effect of ventilation on acid-base balance and oxygenation in low bloodflow states. Crit Care Med, 1994, 22(11): 1827-34.
[95] Kern KB, Sanders AB, Voorhees WD et al. Changes in expired end-tidal carbon dioxide during cardiopulmonary resuscitation in dogs: a prognostic guide for resuscitation efforts. J Am Coll Cardiol, 1989,13(5): 1184-9.
[96] Chase PB, Kern KB, Sanders AB et al. Effects of graded doses of epinephrine on both noninvasive and invasive measures of myocardial perfusion and blood flow during cardiopulmonary resuscitation. Crit Care Med, 1993, 21(3):413-9.
[97] Weaver WD, Cobb LA, Dennis D et al. Amplitude of ventricular fibrillation waveform and outcome after cardiac arrest. Ann Intern Med, 1985,102:53-5.
[98] Callaham M, Braun O, Valentine W et al. Pre-hospital cardiac arrest treated by urban first-responders: profile of patient response and prediction of outcome by ventricular fibrillation waveform. Ann Emerg Med, 1993,22:1664-77.
[99] Brown CG, Dzwonczyk R, Werman HA et al. Estimating the duration of ventricular fibrillation. Ann Emerg Med, 1989,18:1181-5.
[100] Strohmenger HU, Lindner KH, Keller A et al. Spectral analysis of ventricular fibrillation and closed-chest cardiopulmonary resuscitation. Resuscitation, 1996,33(2):155-61.
[101] Povoas HP, Weil MH, Tang W et al. Predicting the success of defibrillation by electrocardiographic analysis. Resuscitation, 2002,53:77-82.
[102] Eftestol T, Sunde K, Aase SO et al. 'Probability of successful defibrillation' as a monitor during CPR in out-of-hospital cardiac arrested patients. Resuscitation, 2001,48(3):245-54.
[103] Callaway CW, Sherman LD, Mosesso VN et al. Scaling exponent predicts defibrillation success for out-of-hospital ventricular fibrillation cardiac arrest. Circulation, 2001,103(12):1656- 61.
[104] Hamprecht FA, Achleitner U, Krismer AC et al. Fibrillation power, an alternative method of ECG spectral analysis for prediction of countershock success in a porcine model of ventricular fibrillation. Resuscitation, 2001, 50(3):287-96.
[105] Hamzei A, Ohara T, Kim YH et al. The Role of Approximate Entropy in Predicting Ventricular Defibrillation Threshold. J Cardiovasc Pharmacol Ther, 2002,7(1):45-52.
[106] Stwart GN. Researches on the circulation time and on the influences which affect it. J Physiol, 1897-1898,22:158-183.
[107] Nyboer J, Bango S, Barnett A et al. Radiocardiograms - the electrical impedance changes of the heart in relation to electrocardiograms and heart sounds. J. Clin. Invest, 1940, 19: 963.
[108] Lee E.Baker. Application of the Impedance to Respiratory system. IEEE transactions on biomedical engineering, 1989,3:50-52.
[109] Baker LE. Principles of the Impedance Technique IEEE transaction on biomedical engineering, 1989,11.
[110] Van der Berg J, Alberts AJ. Limitations of electrical impedance plethysmography. Circ Res, 1954,2:333-339.
[111] Allison RD, Holes EI, Nyboer J. Volumetric dynamics of respiration as measured by electrical impedance plethysmography. J Appl Physiol, 1964,19:199-173.
[112] Powers SR, Schaffer C, Boba A et al: Physical and biologic factors in impedance plethysmography, Suregery, 1958,44:53-61.
[113] Geddes LA, Hoff HE, Mello A et al. Continuous measurement of ventricular stroke volume by electrical impedance. Cardiovasc Res Center Bull, 1966,4:118-130.
[114] Haryadi DG, Westenskow DR. Noninvasive thoracic bioimpedance cardiac output from ensemble averaged impedance cardiograms: Comparison with thermodilution. J Clin Monit, 1997,13:417-417.
[115] R.B.Raza, R.P. Patterson, and L.Wang. Filtering respiration and low frequency movement artifacts from the cardiographic electrical impedance signal. Med, Biol. Eng. Comput, 1992,30:556-561.
[116] J.H.Nagel, L.Y.Shyu, S.P.Reddy et al. New signal processing techniques for improved precision of noninvasive impedance cardiography. Ann. Biomed. Eng, 1989,5:517-534.
[117] X.Wang, H.H. Sun, Joseph M. Van De Water. An advanced signal processing technique for impedance cardiography. IEEE Tranactions on biomedical engineering, 1995,42:224-230.
[118] D.P.Bernstein, H.J.M.Lemmens. Stroke volume equation for impedance cardiography. Med. Biol.Eng.Comput, 2005,43:443-450.
[119] Quail, AW, Traugott, et al. Thoracic resistivity for stroke volume determination in impedance cardiography. J. Appl. Physiol, 1981,50:191-195.
[120] Water VD, MILLER JM et al. Impedance cardiography: the next vital sign technology. Chest, 2003,123: 2028-2033.
[121] Pellis T, Bisera J, Tang W et al. Expanding automatic external defibrillators to include automated detection of cardiac respiratory, and cardiorespiratory arrest. Crit Care Med, 2002,30:S176-S178.
[122] M. Aboy, J. McNames, T. Thong et al. An automatic beat detection algorithm for pressure signals. IEEE Transactions on Biomedical Engineering, 2005,52(10): 1662-1670.
[123] M. Aboy, C. Crespo, J. McNames et al. utomatic detection algorithm for physiolgic pressure signal components. Proceedings of the Second Joint EMBS/BMES Conference 2002, Houston, TX, 23-26 October 2002, pp. 23-26.
[124] M. Aboy, J. McNames, B. Goldstein. Automatic detection algorithm of intracranial pressure waveform components. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, Istanbul, Turkey, 2001, 25-28(3): 2231-2234.
[125] P. D. Welch. The use of fast Fourier transforms for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics, 1967, 15:70-73.
[126] K. Beliaev, N. Kuzminyh. Breath waves filtration in bioimpedance cardiography. Euro Conference BIOSIGNAL 2000, Brno, Czech Republic.
[127] 李永勤．糖尿病早期自主神经病变的无创检测方法研究，第一军医大学硕士毕业论文，2003。
[128] Research Resources for Complex Physiologic Signals National Center for Research Resources of the National Institutes of Health. http://www.physionet.org/physiobank/database/cudb.
[129] Carlisle EJF, Allen JD, Kernohan WG, et al. Fourier analysis ofventricular fibrillation of varied aetiology. Eur Heart J, 1990, 11:173-181.
[2] Langhelle A, Nolan J, Herlitz J et al. 2003 Utstein Consensus Symposium. Recommended guidelines for reviewing, reporting, and conducting research on post-resuscitation care: the Utstein style. Resuscitation, 2005, 66(3):271-83.
[3] Culley LL, Rea TD, Murray JA et al. Public access defibrillation in out-of-hospital cardiac arrest: a community-based study. Circulation, 2004, 109:1859-63.
[4] Chugh SS, Jui J, Gunson K et al. Current burden of sudden cardiac death: multiple source surveillance versus retrospective death certificate- based review in a large US community. J Am Coll Cardiol, 2004, 44:1268 -1275.
[5] Vaillancourt C, Stiell IG. Cardiac arrest care and emergency medical services in Canada. Can J Cardiol, 2004,20:1081-1090.
[6] Eisenberg MS, Mengert TJ. Cardiac resuscitation. N Engl J Med, 2001, 344(17): 1304-13.
[7] Herlitz J, Bahr J, Fischer M et al. Resuscitation in Europe: a tale of five European regions. Resuscitation, 1999, 41(2):121-31.
[8] Becker LB. Epidemiology: Etiology, Incidence, and Survival Rates. In M.H. Weil, W. Tang, and A. Ross, editors, CPR: Resuscitation of the arrested heart, pages 13-27. W.B. Saunders Company, Philadelphia, 1999.
[9] Becker LB, Ostrander MP, Barrett J et al. Outcome of CPR in a large metropolitan area-where are the survivors? Ann Emerg Med, 1991, 20(4):355-61.
[10] Lombardi G, Gallagher J, and Gennis P. Outcome of out-of-hospital cardiac arrest in New York City. The Pre-Hospital Arrest Survival Evaluation (PHASE) Study. JAMA, 1994, 271(9):678-83.
[11] Weil MH, Tang W, Bisera J. Cardiopulmonary resuscitation: one size does not fit all. Circulation, 2003, 107(6):794.
[12] Jin X, Pernat A, Bisera J. Electrical Causes of Cardiac Arrest and Their Managment. In M.H. Weil, W. Tang, and A. Ross, editors, CPR: Resuscitation of the arrested heart., pages 37-51. W.B. Saunders Company, Philadelphia, 1999.
[13] Tang W, Weil MH, Sun S et al. The effects of biphasic and conventional monophasic defibrillation on postresuscitation myocardial function. J Am Coll Cardiol, 1999, 34(3):815-22.
[14] Xie J, Weil MH, Sun S et al. High-energy defibrillation increases the severity of postresuscitation myocardial dysfunction. Circulation, 1997, 96(2):683-8.
[15] Deshmukh HG, Weil MH, Gudipati CV et al. Mechanism of blood flow generated by precordial compression during CPR. I. Studies on closed chest precordial compression. Chest, 1989, 95(5): 1092-9.
[16] Larsen MP, Eisenberg MS, Cummins RO et al. Predicting survival from out-of-hospital cardiac arrest: a graphic model. Ann Emerg Med, 1993, 22:1652-1658.
[17] Valenzuela TD, Roe DJ, Cretin S et al. Estimating effectiveness of cardiac arrest interventions: a logistic regression survival model. Circulation, 1997, 96:3308-3313.
[18] Holmberg M, Holmberg S, Herlitz J. Factors modifying the effect of bystander cardiopulmonary resuscitation on survival in out-of-hospital cardiac arrest patients in Sweden. Eur Heart J, 2001, 22: 511-519.
[19] Holmberg M, Holmberg S, Herlitz J et al. Survival after cardiac arrest outside hospital in Sweden. Swedish Cardiac Arrest Registry. Resuscitation, 1998, 36:29 -36.
[20] Cobb LA, Fahrenbruch CE, Walsh TR et al. Influence of cardiopulmonary resuscitation prior to defibrillation in patients with out-of-hospital ventricular fibrillation. JAMA, 1999,281:1182-1188.
[21] Wik L, Hansen TB, Fylling F et al. Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial. JAMA, 2003, 289:1389 -1395.
[22] White RD, Russell JK. Refibrillation, resuscitation and survival in out-of-hospital sudden cardiac arrest victims treated with biphasic automated external defibrillators. Resuscitation, 2002, 55:17-23.
[23] Carpenter J, Rea TD, Murray JA, et al. Defibrillation waveform and post-shock rhythm in out-of-hospital ventricular fibrillation cardiac arrest. Resuscitation, 2003,59:189-196.
[24] Berg MD, Clark LL, Valenzuela TD et al. Post-shock chest compression delays with automated external defibrillator use. Resucitation,2005,64:287-291.
[25] Appleton GO, Cummins RO, Larson MP et al. CPR and the single rescuer: at what age should you "call first" rather than "call fast"? Ann Emerg Med, 1995, 25:492-494.
[26] Hickey RW, Cohen DM, Strausbaugh S et al. Pediatric patients requiring CPR in the prehospital setting. Ann Emerg Med, 1995, 25:495-501.
[27] Mogayzel C, Quan L, Graves JR et al. Out-of-hospital ventricular fibrillation in children and adolescents: causes and outcomes. Ann Emerg Med, 1995, 25:484-491.
[28] Berg RA, Hilwig RW, Kern KB et al. Simulated mouth-to-mouth ventilation and chest compressions (bystander cardiopulmonary resuscitation) improves outcome in a swine model of prehospital pediatric asphyxial cardiac arrest. Crit Care Med, 1999,27:1893-1899.
[29] Berg RA, Hilwig RW, Kern KB et al. "Bystander" chest compressions and assisted ventilation independently improve outcome from piglet asphyxial pulseless "cardiac arrest." Circulation, 2000, 101:1743-1748.
[30] 2005 American Heart Association Guidelines for Cardiopulmonary Resuscitatiuon and Emergency Cardiovascular Care. Circulation, 2005, 112 [Suppl I]:IV-19-IV-34.
[31] 2005 American Heart Association Guidelines for Cardiopulmonary Resuscitatiuon and Emergency Cardiovascular Care. Circulation, 2005, 112 [Suppl I]: IV-58 - IV-66.
[32] K.B. Kern and J.T. Niemann. Coronary perfusion pressure during cardiopulmonary resuscitation. In N.A. Paradis, H.R. Halperin, and R.M. Nowak, editors, Cardiac arrest: The science and practice of resuscitation medicine. chapter 14 A, pages 270-285. Williams & Wilkins, Baltimore, 1996.
[33] Kern KB, Ewy GA, Voorhees WD et al. Myocardial perfusion pressure: a predictor of 24-hour survival during prolonged cardiac arrest in dogs. Resuscitation, 1988,16(4):241-50.
[34] Paradis NA, Martin GB, Rivers EP et al. Coronary perfusion pressure and the return of spontaneous circulation in human cardiopulmonary resuscitation. JAMA, 1990, 263(8): 1106-13.
[35] Weil MH, Bisera J, Trevino RP et al. Cardiac output and end-tidal carbon dioxide. Crit Care Med, 1985,13(11):907-9.
[36] Reed MJ, Clegg GR, Robertson CE. Analysing the ventricular fibrillation waveform. Resuscitation, 2003, 57(1):11-20.
[37] Yu T, Weil MH, Tang W et al. Adverse outcomes of interrupted precordial compression during automated defibrillation. Circulation, 2002,106(3):368-72.
[38] Eftestol T, Sunde K, Steen PA. Effects of interrupting precordial compressions on the calculated probability of defibrillation success during out-of-hospital cardiac arrest. Circulation, 2002, 105:2270-3.
[39] Cobb LA, Fahrenbruch CE, Walsh TR et al. Influence of CPR prior to defibrillation in patients with out-of-hospital ventricular fibrillation. JAMA, 1999,281(13): 1182-8.
[40] Wik L, Hansen TB, Fylling F et al. Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial. JAMA, 2003,289(11):1389-95.
[41] Kern KB, Hilwig RW, Berg RA et al. Importance of continuous chest compressions during CPR: improved outcome during a simulated single lay-rescuer scenario. Circulation, 2002,105(5):645-9.
[42] Wik L, Steen PA, Bircher NG. Quality of bystander cardiopulmonary resuscitation influences outcome after prehospital cardiac arrest. Resuscitation, 1994,28:195-203.
[43] Gallagher EJ, Lombardi G, Gennis P. Effectiveness of bystander cardiopulmonary resuscitation and survival following out-of-hospital cardiac arrest. JAMA, 1995, 274: 1922-1925
[44] Van Hoeyweghen EJ, Bossaret LL, Mullie A et al. Quality and efficiency of bystander CPR. Belgian Cerebral Resuscitation Study Group. Resuscitation, 1993, 26: 47-52
[45] Brown TB, Dias JA, Saini D et al. Relationship between knowledge of cardiopulmonary resuscitation guidelines and performance. Resuscitation, 2006, 69(2): 253-61.
[46] Abella BS, Sandbo N, Alvarado JP et al. Quality of cardiopulmonary resuscitation during in-hospital cardiac arrest. JAMA, 2005, 293: 305-10.
[47] Wik L, Kramer-Johansen J, Myklebust H et al. Quality of cardiopulmonary resuscitation during out-of-hospital cardiac arrest. JAMA, 2005, 293: 299-304.
[48] Baubin M, Haid C, Hamm P et al. Measuring forces and frequency during active compression decompression cardiopulmonary resuscitation: a device for training, research and real CPR. Resuscitation, 1999, 43(1): 17-24.
[49] Aase SO, Myklebust H. Compression depth estimation for CPR quality assessment using DSP on accelerometer signals. IEEE Trans Biomed Eng, 2002, 49(3):263-8.
[50] Eftestol T, Sunde K, Steen PA. The effects of interrupting precordial compressions on the calculated probability of defibrillation success during out-of-hospital cardiac arrest. Circulation, 2002,105:2270-3.
[51] Eftestol T, Sunde K, Ole Aase S et al. Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest. Circulation, 2000, 26;102(13):1523-9.
[52] Watson JN, Uchaipichat N, Addison PS et al. Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods.Resuscitation, 2004, 63(3):269-75.
[53] Marn-Pernat A, Weil MH, Tang W et al. Optimizing timing of ventricular defibrillation. Crit Care Med, 2001, 29(12):2360-5.
[54] Menegazzi JJ, Callaway CW, Sherman LD et al. Ventricular fibrillation scaling exponent can guide timing of defibrillation and other therapies. Circulation. 2004 Feb 24;109(7):926-31.
55. Sedgwick ML, Watson J, Dalziel K, Carrington DJ, Cobbe SM. Efficacy of out of hospital defibrillation by ambulance technicians using automated external defibrillators. The Heartstart Scotland Project. Resuscitation, 1992, 24(1):73-87.
[56] Clifford AC. Comparative assessment of shockable ECG rhythm detection algorithms in automated external defibrillators. Resuscitation, 1996, 32(3):217-25.
[57] Thakor NV, Sheng ZY, and Yan PK. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE. Trans. Biomed. Eng, 1990,37(9), 837-843.
[58] Chen SW, Clarkson PM, and Fan Q. A robust sequential detection algorithm for cardiac arrhythmias classification. IEEE. Trans. Biomed. Eng., 1996, 43, no. 11, 1120-1125.
[59] Lin D, Dicarlo LJ, and Jenkins JM. Identification of ventricular tachycardia using interactivity ventricular electrograms analysis of time and frequency domain patterns. PACE, 1988, 11, 1592-1606.
[60] Clayton R, Murray A, and Campell R. Changes in the surface ECG frequency spectrum during the onset of ventricular fibrillation. in Proceedings on Computers in Cardiology. Los Alamos, CA: IEEE Computer Society Press, 1991,515-518.
[61] Afono VX and Becker WJ. Detecting ventricular fibrillation, Selecting the appropriate time frequency analysis tool for the application. Inst. Elect. Eng, 1995, 152-159.
[62] Khadra L, Al-Fahoum AS, and Al-Nashash H. Detection of life-threatening cardiac arrhythmiass using wavelet transformation. Inst. Elect. Eng. Med. Biol. Eng. Comput, 1997, 35, pp. 626-632.
[63] Lawandowski P, Meste O, Maniewski R et al. Risk evaluation of ventricular tachycardia using wavelet transform irregularity of the high-resolution electrocardiogram. Inst. Elect. Eng. Med. Biol. Eng. Comput, 2000, 38, 666-673.
[64] Zhang XS, Zhu YS, Thakor NV et al. Detecing ventricular tachycardia and fibrillation by complexity measure. IEEE. Trans. Biomed. Eng, 1999, 46(5):548-555.
[65] Felice MR, Richard JP, andKristina MR. Identification of ECG Arrhythmiass Using Phase Space Reconstruction. PKDD, 2001, 2168:411-423.
[66] Paul SA.Wavelet transforms and the ECG: a review. Physiol. Meas, 2005, 26, pp. 188-199.
[67] Romero L, Addison PS, and Grubb N. R-wave detection using continuous wavelet modulus maxima. Computers in Cardiology, 2003, 30: 565-568.
[68] Shubha K, Robin M and Faye GB. Wavelet transform-based QRS Complex detector. IEEE transactions on biomedical engineering, 1999, 46: 838-848.
[69] Fitzgibbon E, Berger R, Tsitlik J et al. Determination of the noise source in the electrocardiogram during cardiopulmonary resuscitation. Crit Care Med, 2002, 30: 148-153.
[70] Langhelle A, Eftestol T, Myklebust H et al. Reducing CPR artifacts in ventricular fibrillation in vitro. Resuscitation, 2001, 48:279-291.
[71] Watson JN, Addison PS, Clegg GR et al. A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation. Resuscitation, 2000, 43:121-127.
[72] Strohmenger HU, Aalen O, and Brown CG. Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans. Chest, 1997,111:1584-589.
[73] Aase SO, Eftestol T, Husoy HJ et al. CPR artifact removal from human ECG using optimal multichannel filtering. IEEE Trans Biomed Engr, 2000, 47: 1440-1449.
[74] Husoy HJ, Eilevstjonn J, Eftestol T et al. Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm. IEEE Trans Biomed Eng, 2002,49:1287-1298.
[75] Grossmann A. Wavelet transforms and edge detection. In S. A. et al., editor, Stochastic Processes in Physics and Engineering, 149—157. D. Reidel Publishing Company, 1988.
[76] Li C, Zheng C, and Tai C. Detection of ECG characteristic points using wavelet transforms. 1995, 42: 21-28.
[77] Daubechies I. Ten lectures on wavelets. CBMS, SIAM, 1994, 61:194-202.
[78] Zoran N and Joel WB. Spike detection using the continuous wavelet transform. IEEE transactions on biomedical engineering, 2005, 52(1): 74-87.
[79] Malletand S and Hwang WL. Singularity detetion and processing with wavelets. IEEE Trans. Inform. Theory, 1992, 38(2):617-643.
[80] Bruce LM and Adhami RR. Classifying mammographic mass shapes using the wavelet transform modulus maxima method. IEEE Trans. Med. Imaging, 1999, 18:1170-7.
[81] Povoas HP, Bisera J. Electrocardiographic waveform analysis for predicting the success of defibrillation. Crit Care Med, 2000, 28: 210-211.
[82] Marn-Pernat S, Weil MH, Tang W et al. Optimizing timing of ventricular defibrillation. Crit Care Med, 2001, 29: 2360-2365.
[83] Young C, Bisera J, Gehman S et al. Amplitude spectrum area: Measuring the probability of successful defibrillation as applied to human data. Crit Care Med, 2004, 32:356-358.
[84] Kuo S and Dillman R. Computer detection of ventricular fibrillation. Computers in Cardiology, IEEE Computer Society, 1978, 347-349.
[85] Barro S, Ruiz R, Cabello D et al. Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmiass and imitative artifacts: a diagnostic system. Journ. Biomed. Eng, 1989,11:320-328.
[86] Jekova I. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. Physiol. Meas, 2000,21:429-439.
[87] Anton A, Robert T, and Kal U. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. BioMedical Engineering OnLine, 2005,4:60 doi:10.1186/1475-925X-4-60.
[88] Noordergraaf GJ, Drinkwaard BW, Van Berkom PF et al. The quality of chest compressions by trained personnel: the effectof feedback, via the CPREzy, in a randomized controlled trial using a manikin model. Resuscitation, 2006, 69:241-52.
[89] Kette F, Weil MH, Gazmuri RJ. Buffer solutions may compromise cardiac resuscitation by reducing coronary perfusion pressure. JAMA, 1991, 266: 2121-2126.
[90] Tang W, Weil MH, Sun S, Gazmuri RJ, Bisera J. Progressive myocardial dysfunction after cardiac resuscitation. Crit Care Med 1993;21:1046-1050.
[91] Tang W, Weil MH, Sun RJ et al. The effects of biphasic and conventional monophasic defibrillation on post resuscitation myocardial function. J Am Coll Cardiol, 1999,4:815-822.
[92] Nieman JT, Criley JM, Rosborough JP et al. Predictive indices of successful cardiac resuscitation after prolonged arrest and experimental cardiopulmonary resuscitation. Ann Emerg Med, 1985,14:521-528.
[93] Gudipati CV, Weil MH, Bisera J et al. Expired carbon dioxide: a noninvasive monitor of cardiopulmonary resuscitation. Circulation, 1988, 77(l):234-9.
[94] Idris AH, Staples ED, O'Brien DJ et al. Effect of ventilation on acid-base balance and oxygenation in low bloodflow states. Crit Care Med, 1994, 22(11): 1827-34.
[95] Kern KB, Sanders AB, Voorhees WD et al. Changes in expired end-tidal carbon dioxide during cardiopulmonary resuscitation in dogs: a prognostic guide for resuscitation efforts. J Am Coll Cardiol, 1989,13(5): 1184-9.
[96] Chase PB, Kern KB, Sanders AB et al. Effects of graded doses of epinephrine on both noninvasive and invasive measures of myocardial perfusion and blood flow during cardiopulmonary resuscitation. Crit Care Med, 1993, 21(3):413-9.
[97] Weaver WD, Cobb LA, Dennis D et al. Amplitude of ventricular fibrillation waveform and outcome after cardiac arrest. Ann Intern Med, 1985,102:53-5.
[98] Callaham M, Braun O, Valentine W et al. Pre-hospital cardiac arrest treated by urban first-responders: profile of patient response and prediction of outcome by ventricular fibrillation waveform. Ann Emerg Med, 1993,22:1664-77.
[99] Brown CG, Dzwonczyk R, Werman HA et al. Estimating the duration of ventricular fibrillation. Ann Emerg Med, 1989,18:1181-5.
[100] Strohmenger HU, Lindner KH, Keller A et al. Spectral analysis of ventricular fibrillation and closed-chest cardiopulmonary resuscitation. Resuscitation, 1996,33(2):155-61.
[101] Povoas HP, Weil MH, Tang W et al. Predicting the success of defibrillation by electrocardiographic analysis. Resuscitation, 2002,53:77-82.
[102] Eftestol T, Sunde K, Aase SO et al. 'Probability of successful defibrillation' as a monitor during CPR in out-of-hospital cardiac arrested patients. Resuscitation, 2001,48(3):245-54.
[103] Callaway CW, Sherman LD, Mosesso VN et al. Scaling exponent predicts defibrillation success for out-of-hospital ventricular fibrillation cardiac arrest. Circulation, 2001,103(12):1656- 61.
[104] Hamprecht FA, Achleitner U, Krismer AC et al. Fibrillation power, an alternative method of ECG spectral analysis for prediction of countershock success in a porcine model of ventricular fibrillation. Resuscitation, 2001, 50(3):287-96.
[105] Hamzei A, Ohara T, Kim YH et al. The Role of Approximate Entropy in Predicting Ventricular Defibrillation Threshold. J Cardiovasc Pharmacol Ther, 2002,7(1):45-52.
[106] Stwart GN. Researches on the circulation time and on the influences which affect it. J Physiol, 1897-1898,22:158-183.
[107] Nyboer J, Bango S, Barnett A et al. Radiocardiograms - the electrical impedance changes of the heart in relation to electrocardiograms and heart sounds. J. Clin. Invest, 1940, 19: 963.
[108] Lee E.Baker. Application of the Impedance to Respiratory system. IEEE transactions on biomedical engineering, 1989,3:50-52.
[109] Baker LE. Principles of the Impedance Technique IEEE transaction on biomedical engineering, 1989,11.
[110] Van der Berg J, Alberts AJ. Limitations of electrical impedance plethysmography. Circ Res, 1954,2:333-339.
[111] Allison RD, Holes EI, Nyboer J. Volumetric dynamics of respiration as measured by electrical impedance plethysmography. J Appl Physiol, 1964,19:199-173.
[112] Powers SR, Schaffer C, Boba A et al: Physical and biologic factors in impedance plethysmography, Suregery, 1958,44:53-61.
[113] Geddes LA, Hoff HE, Mello A et al. Continuous measurement of ventricular stroke volume by electrical impedance. Cardiovasc Res Center Bull, 1966,4:118-130.
[114] Haryadi DG, Westenskow DR. Noninvasive thoracic bioimpedance cardiac output from ensemble averaged impedance cardiograms: Comparison with thermodilution. J Clin Monit, 1997,13:417-417.
[115] R.B.Raza, R.P. Patterson, and L.Wang. Filtering respiration and low frequency movement artifacts from the cardiographic electrical impedance signal. Med, Biol. Eng. Comput, 1992,30:556-561.
[116] J.H.Nagel, L.Y.Shyu, S.P.Reddy et al. New signal processing techniques for improved precision of noninvasive impedance cardiography. Ann. Biomed. Eng, 1989,5:517-534.
[117] X.Wang, H.H. Sun, Joseph M. Van De Water. An advanced signal processing technique for impedance cardiography. IEEE Tranactions on biomedical engineering, 1995,42:224-230.
[118] D.P.Bernstein, H.J.M.Lemmens. Stroke volume equation for impedance cardiography. Med. Biol.Eng.Comput, 2005,43:443-450.
[119] Quail, AW, Traugott, et al. Thoracic resistivity for stroke volume determination in impedance cardiography. J. Appl. Physiol, 1981,50:191-195.
[120] Water VD, MILLER JM et al. Impedance cardiography: the next vital sign technology. Chest, 2003,123: 2028-2033.
[121] Pellis T, Bisera J, Tang W et al. Expanding automatic external defibrillators to include automated detection of cardiac respiratory, and cardiorespiratory arrest. Crit Care Med, 2002,30:S176-S178.
[122] M. Aboy, J. McNames, T. Thong et al. An automatic beat detection algorithm for pressure signals. IEEE Transactions on Biomedical Engineering, 2005,52(10): 1662-1670.
[123] M. Aboy, C. Crespo, J. McNames et al. utomatic detection algorithm for physiolgic pressure signal components. Proceedings of the Second Joint EMBS/BMES Conference 2002, Houston, TX, 23-26 October 2002, pp. 23-26.
[124] M. Aboy, J. McNames, B. Goldstein. Automatic detection algorithm of intracranial pressure waveform components. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, Istanbul, Turkey, 2001, 25-28(3): 2231-2234.
[125] P. D. Welch. The use of fast Fourier transforms for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics, 1967, 15:70-73.
[126] K. Beliaev, N. Kuzminyh. Breath waves filtration in bioimpedance cardiography. Euro Conference BIOSIGNAL 2000, Brno, Czech Republic.
[127] 李永勤．糖尿病早期自主神经病变的无创检测方法研究，第一军医大学硕士毕业论文，2003。
[128] Research Resources for Complex Physiologic Signals National Center for Research Resources of the National Institutes of Health. http://www.physionet.org/physiobank/database/cudb.
[129] Carlisle EJF, Allen JD, Kernohan WG, et al. Fourier analysis ofventricular fibrillation of varied aetiology. Eur Heart J, 1990, 11:173-181.
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