Chiang C M, Jenkins J M, DiCarlo L A
Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor.
Pacing Clin Electrophysiol. 1994 Aug;17(8):1373-9. doi: 10.1111/j.1540-8159.1994.tb02456.x.
The adoption of digital signal processing (DSP) microchips for detection and analysis of electrocardiographic signals offers a means for increased computational speed and the opportunity for design of customized architecture to address real-time requirements. A system using the Motorola 56001 DSP chip has been designed to realize cycle-by-cycle detection (triggering) and waveform analysis using a time-domain template matching technique, correlation waveform analysis (CWA). The system digitally samples an electrocardiographic signal at 1000 Hz, incorporates an adaptive trigger for detection of cardiac events, and classifies each waveform as normal or abnormal. Ten paired sets of single-chamber bipolar intracardiac electrograms (1-500 Hz) were processed with each pair containing a sinus rhythm (SR) passage and a corresponding arrhythmia segment from the same patient. Four of ten paired sets contained intraatrial electrograms that exhibited retrograde atrial conduction during ventricular pacing; the remaining six paired sets of intraventricular electrograms consisted of either ventricular tachycardia (4) or paced ventricular rhythm (2). Of 2,978 depolarizations in the test set, the adaptive trigger failed to detect 6 (99.8% detection sensitivity) and had 11 false triggers (99.6% specificity). Using patient dependent thresholds for CWA to classify waveforms, the program correctly identified 1,175 of 1,197 (98.2% specificity) sinus rhythm depolarizations and 1,771 of 1,781 (99.4% sensitivity) abnormal depolarizations. From the results, the algorithm appears to hold potential for applications such as real-time monitoring of electrophysiology studies or detection and classification of tachycardias in implantable antitachycardia devices.
采用数字信号处理（DSP）微芯片来检测和分析心电图信号，为提高计算速度提供了一种手段，也为设计定制架构以满足实时需求创造了机会。已设计出一种使用摩托罗拉56001 DSP芯片的系统，该系统利用时域模板匹配技术——相关波形分析（CWA）来实现逐周期检测（触发）和波形分析。该系统以1000 Hz的频率对心电图信号进行数字采样，采用自适应触发来检测心脏事件，并将每个波形分类为正常或异常。对十组配对的单腔双极心内心电图（1 - 500 Hz）进行了处理，每组包含来自同一患者的窦性心律（SR）段和相应的心律失常段。十组配对数据中有四组包含在心室起搏期间表现出逆行心房传导的心房内心电图；其余六组配对的心室内电图由室性心动过速（4组）或起搏心室节律（2组）组成。在测试集中的2978次去极化中，自适应触发未能检测到6次（检测灵敏度为99.8%），有11次误触发（特异性为99.6%）。使用依赖于患者的CWA阈值对波形进行分类，该程序正确识别出1197次窦性心律去极化中的1175次（特异性为98.2%）以及1781次异常去极化中的1771次（灵敏度为99.4%）。从结果来看，该算法在诸如电生理研究的实时监测或植入式抗心动过速设备中快速心律失常的检测和分类等应用方面似乎具有潜力。
