在心肺复苏期间,由于胸外按压对心电(ECG)信号产生了机械干扰,无法可靠地辨识心电节律。而中断胸外按压会减小复苏成功的可能性,所以本文研究开发了一个新的滤波算法——增强最小均方(eLMS)算法,可以在无需硬件参考信号支持的情况下,成功滤除胸外按压干扰,达到在不间断胸外按压的情况下正确辨识室颤(VF)节律和正常窦性(SR)节律。该滤波算法仅用受按压干扰的心电(cECG)信号实现滤波,无需其它参考信号。通过在不同信噪比的情况下混合ECG信号和按压干扰信号来验证该算法,并且与其它已经成熟的算法比较。验证结果表明在不同信噪比情况下,eLMS方法的辨识结果均优于其他方法。进一步研究表明,仅用cECG信号就可以很好地辨识心电节律。本算法的成功研发降低了体外除颤仪的研发成本,提高了心电节律辨识的准确性以及复苏成功的可能性。 During cardiopulmonary resuscitation, the cardiogram can not be reliably identified due to mechanical interference with the ECG signal caused by chest compressions. The interruption of chest compressions will reduce the possibility of successful resuscitation. Therefore, this paper developed a new filtering algorithm - enhanced least mean square (eLMS) algorithm, which can successfully filter the chest without hardware reference signal support External press interference to achieve the correct identification of ventricular fibrillation (VF) rhythm and normal sinus rhythm (rhythm) without interruption of chest compressions. This filtering algorithm uses only cECG signals that are subject to press interference to filter without the need for additional reference signals. The algorithm is validated by mixing ECG signals and pressing jamming signals at different signal-to-noise ratios and compared to other algorithms that are already well established. The verification results show that the eLMS method has better identification results than other methods under different SNRs. Further studies have shown that the ECG rhythm can be well identified using only cECG signals. The successful development of this algorithm reduces the R & D cost of the external defibrillator, improves the accuracy of ECG rhythm identification and the possibility of successful resuscitation.