Coast D A, Stern R M, Cano G G, Briller S A
Allegheny-Singer Research Institute, Pittsburgh, PA 15212.
IEEE Trans Biomed Eng. 1990 Sep;37(9):826-36. doi: 10.1109/10.58593.
This paper describes a new approach to ECG arrhythmia analysis based on "hidden Markov modeling" (HMM), a technique successfully used since the mid-1970's to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The hidden Markov modeling approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum likelihood reestimation algorithm. Initial results suggest that this approach may provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat including the P wave.
本文描述了一种基于“隐马尔可夫模型”（HMM）的心电图心律失常分析新方法。自20世纪70年代中期以来，该技术已成功用于为自动语音识别对语音波形进行建模。许多室性心律失常可通过检测和分析QRS复合波并确定R-R间期来分类。然而，室上性心律失常的分类除了QRS复合波外，通常还需要检测P波。隐马尔可夫建模方法在单个参数模型中结合了心电图信号的结构和统计知识。使用迭代最大似然重估算法从训练数据中估计模型参数。初步结果表明，这种方法可能通过准确表示包括P波在内的整个心搏，改进室上性心律失常分析。
