Evans S J, Hastings H, Bodenheimer M M
Harris Chasanoff Heart Institute, Department of Medicine, Long Island Jewish Medical Center, New Hyde Park, NY 11042.
Pacing Clin Electrophysiol. 1994 Apr;17(4 Pt 1):611-26. doi: 10.1111/j.1540-8159.1994.tb02398.x.
Despite advances in the computerized detection of arrhythmias, arrhythmia recognition by morphological waveform analysis still poses a difficult problem. Artificial neural networks, computer algorithms that are self-trained by an analog of biological synaptic modification to perform pattern recognition, hold great promise for the differentiation of various cardiac rhythms. The goal of this study was to differentiate beats of sinus and ventricular origin on a global basis and on a patient-specific basis by the use of artificial neural network analysis. Neural networks were trained to recognize digitized intracardiac electrograms (9 patients) and surface electrocardiograms (11 patients) obtained during sinus rhythm and ventricular tachycardia. After training, sinus rhythm or ventricular tachycardia beats were input into the neural network, and classified as to their origin. By the use of modified receiver operating characteristic curve plots, it was possible to differentiate with high sensitivity and specificity between beats of sinus origin and ventricular origin in all patients. The addition of high amounts of noise to the beats did not markedly degrade the performance of the surface ECG neural networks, and still allowed high sensitivity in differentiating beats of sinus origin from beats of ventricular origin, especially when noise was added to the training set. Neural networks provided sensitive and specific detection of cardiac electrical activity during sinus rhythm and ventricular tachycardia, and may play an important role in allowing development of improved arrhythmia recognition and management systems.
尽管在心律失常的计算机检测方面取得了进展，但通过形态波形分析识别心律失常仍然是一个难题。人工神经网络是通过类似生物突触修饰的方式进行自我训练以执行模式识别的计算机算法，在区分各种心律方面具有很大的潜力。本研究的目的是通过人工神经网络分析在整体基础上以及针对特定患者的基础上区分窦性和室性起源的搏动。神经网络经过训练以识别在窦性心律和室性心动过速期间获得的数字化心内电图（9例患者）和体表心电图（11例患者）。训练后，将窦性心律或室性心动过速搏动输入神经网络，并对其起源进行分类。通过使用改良的受试者工作特征曲线，能够在所有患者中以高灵敏度和特异性区分窦性起源和室性起源的搏动。向搏动中添加大量噪声并不会显著降低体表心电图神经网络的性能，并且在区分窦性起源的搏动和室性起源的搏动时仍能保持高灵敏度，尤其是当噪声添加到训练集中时。神经网络能够灵敏且特异地检测窦性心律和室性心动过速期间的心脏电活动，并且可能在改进心律失常识别和管理系统的开发中发挥重要作用。
