Coast D A, Cano G G, Briller S A
Allegheny-Singer Research Institute, Pittsburgh, Pennsylvania 15212-9986.
J Electrocardiol. 1990;23 Suppl:184-91. doi: 10.1016/0022-0736(90)90099-n.
Hidden Markov modelling (HMM) is a powerful stochastic modelling technique that has been successfully applied to automatic speech recognition problems. We are currently investigating the application of HMM to electrocardiographic signal analysis with the goal of improving ambulatory ECG analysis. The HMM approach specifies a Markov chain to model a "hidden" sequence that in this case is the underlying state of the heart. Each state of the Markov chain has an associated output function that describes the statistical characteristics of measurement samples generated during that state. Given a measurement sequence and HMM parameter estimates, the most likely underlying state sequence can be determined and used to infer beat classification. Advantages of this approach include resistance to noise, ability to model low-amplitude waveforms such as the P wave, and availability of an algorithm for automatically estimating model parameters from training data. We have applied the HMM approach to QRS complex detection and to arrhythmia analysis with encouraging results.
隐马尔可夫模型（HMM）是一种强大的随机建模技术，已成功应用于自动语音识别问题。我们目前正在研究HMM在心电图信号分析中的应用，目的是改进动态心电图分析。HMM方法指定一个马尔可夫链来对一个“隐藏”序列进行建模，在这种情况下，该序列就是心脏的潜在状态。马尔可夫链的每个状态都有一个相关的输出函数，该函数描述了在该状态期间生成的测量样本的统计特征。给定一个测量序列和HMM参数估计值，就可以确定最可能的潜在状态序列，并用于推断心跳分类。这种方法的优点包括抗噪声能力、对低振幅波形（如P波）进行建模的能力，以及有一个从训练数据中自动估计模型参数的算法。我们已将HMM方法应用于QRS波群检测和心律失常分析，并取得了令人鼓舞的结果。
