Koski A
Department of Computer Science, University of Turku, Finland.
Artif Intell Med. 1996 Oct;8(5):453-71. doi: 10.1016/S0933-3657(96)00352-1.
In this paper, we have studied the use of continuous probability density function hidden Markov models for the ECG signal analysis problem. Our previous work has focused on syntactic pattern recognition methods in signal processing. Hidden Markov model is basically a non-deterministic probabilistic finite state machine, which can be constructed inductively. It has been widely used in speech recognition and DNA modelling. We have found that hidden Markov models are very suitable for ECG recognition and analysis problems and that they are able to model accurately segmented ECG signals.
在本文中，我们研究了使用连续概率密度函数隐马尔可夫模型来解决心电图（ECG）信号分析问题。我们之前的工作主要集中在信号处理中的句法模式识别方法上。隐马尔可夫模型本质上是一种非确定性概率有限状态机，可以通过归纳法构建。它已被广泛应用于语音识别和DNA建模。我们发现隐马尔可夫模型非常适合心电图识别和分析问题，并且能够准确地对分段的心电图信号进行建模。
