Finelli C J
Department of Electrical and Computer Engineering, GMI Engineering and Management Institute, Flint, MI 48504-4898, USA.
IEEE Trans Biomed Eng. 1996 Aug;43(8):811-9. doi: 10.1109/10.508543.
Implantable antitachycardia devices rely upon schemes for detecting cardiac arrhythmias which utilize rate and its variations; yet rate parameters often identify nonpathologic tachycardias as potentially dangerous and deliver unwarranted therapy. I have developed a predictive filter based upon the time-sequenced adaptive algorithm to be used as a supplement to rate criteria for detecting and identifying serious arrhythmias. The method does not require a fixed template and is independent of a priori patient information. The algorithm also provides arrhythmia diagnosis immediately at the change in rhythm. Algorithmic parameters were determined based upon a training set of patient data, and performance of the technique was evaluated with a completely new test set of 20 arrhythmia passages. The new algorithm yielded a sensitivity and specificity for ventricular tachycardia of 91% and 82% and for ventricular fibrillation of 71% and 93%. Correlation waveform analysis was used to diagnose the same test set of arrhythmias. It yielded a sensitivity and specificity for ventricular tachycardia of 100% and 67% and for ventricular fibrillation of 50% and 100%.
植入式抗心动过速装置依靠检测心律失常的方案，这些方案利用心率及其变化；然而，心率参数常常将非病理性心动过速识别为潜在危险，并进行不必要的治疗。我开发了一种基于时间序列自适应算法的预测滤波器，用作检测和识别严重心律失常的心率标准的补充。该方法不需要固定模板，且独立于先验患者信息。该算法还能在心律变化时立即提供心律失常诊断。算法参数是根据一组患者数据训练集确定的，该技术的性能用一组全新的20个心律失常片段测试集进行评估。新算法对室性心动过速的敏感性和特异性分别为91%和82%，对心室颤动的敏感性和特异性分别为71%和93%。采用相关波形分析对同一心律失常测试集进行诊断。其对室性心动过速的敏感性和特异性分别为100%和67%，对心室颤动的敏感性和特异性分别为50%和100%。
