HIDDEN MARKOV MODELS IN HOLTER RECORD ANALYSIS

虽然长期（24小时）心电图监测 门诊病人已成为医疗的一个组成部分， 记录波形的诊断、分析进展不大 自1957年霍尔特引入筛查程序以来， 基于传统模式的计算机系统研究 识别技术和区间分析尚未产生 临床上可靠的霍尔特分析自动化方法。 这 实验室已经采用了句法模式识别， 结合专家系统，成功地分析了大多数 室性心律失常 然而，可靠的计算机检测 P波限制了分析心房肌的句法方法， 心律不齐 P波检测是困难的，因为它是低的， 振幅和容易被门诊常见的伪影掩盖 记录 最近，我们测试了一种叫做“隐藏”的技术 马尔可夫模型”（HMM），在计算机 识别人类语言。 这些初步测试 它在计算机识别 ECG的低振幅波（例如P波），即使在 存在足够的噪声以在视觉上模糊它们。 我们的意图 是在计算机分析中研究这种技术， 霍尔特记录中的心律失常。 我们的最终目标是发展 一个完全计算机化的心律失常分析系统， HMM，如果我们的调查验证适用性。

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.