HIDDEN MARKOV MODELS IN HOLTER RECORD ANALYSIS

虽然长期(24小时)心电监测 门诊病人已经成为医疗体系中不可或缺的一部分 对记录波形的诊断和分析进展甚微 自1957年Holter引入筛查程序以来。 基于传统模式的计算机化系统研究 识别技术和区间分析尚未产生 临床可靠的动态心电图自动分析方法。这 实验室已将句法模式识别应用于 与专家系统相结合，成功分析了 室性心律失常。然而，可靠的计算机检测 P波限制了分析心房的句法方法 心律不齐。P波的检测是困难的，因为它是低的 振幅大，易被门诊常见伪影遮挡 唱片。最近，我们测试了一种名为“隐藏”的技术 马尔可夫模型“(HMM)，在计算机化中相当成功地使用 人类语音的识别。这些初步测试已经 它在计算机识别中显示出惊人的有效性 心电图的低幅度波(如P波)，即使在 有足够的噪音使它们在视觉上变得模糊。我们的意图 就是在计算机分析中研究这项技术 在Holter记录中有心律失常。我们的最终目标是发展 一种完全计算机化的心律失常分析系统 嗯，我们的调查是否应该证实它的适用性。

Although long term (24-Hour) electrocardiographic monitoring of ambulatory patients has become an integral part of medical diagnosis, analysis of recorded waveforms has advanced little since introduction of the screening procedure by Holter in 1957. Research into computerized systems using traditional pattern recognition techniques and interval analysis has not yielded a clinically reliable automated method for Holter analysis. This laboratory has employed syntactical pattern recognition in conjunction with an expert system to successfully analyze most ventricular arrhythmias. However, reliable computer detection of the P wave has limited the syntactic approach in analyzing atrial arrhythmias. P Wave detection is difficult because it is low in amplitude and easily obscured by artifact common in ambulatory records. Recently, we have tested a technique called "hidden Markov modeling" (HMM), used quite successfully in computerized identification of human speech. These preliminary tests have shown it to be surprisingly effective in computer identification of low amplitude waves of the ECG (such as the P wave) even in the presence of sufficient noise to visually obscure them. Our intent is to investigate this technique in the computerized analysis of arrhythmias in Holter records. Our eventual goal is development of a fully computerized system for arrhythmia analysis using both HMM, should our investigation verify applicability.