基于隐马尔科夫模型的滑动窗口投票策略的QRS波群形态识别

A sliding window voting strategy based on hidden Markov model for morphology detection of QRS complex

QRS波群形态识别是心电异常检测的关键环节,是疾病诊断的主要依据。目前的QRS波群形态识别方法存在识别出的形态较少、对参数敏感等问题。为此,提出一种基于隐马尔科夫的滑动窗口投票策略SWVHMM自动识别QRS波群形态。首先,将每个QRS波群划分成4个波段,对各波段设置滑动窗口提取样本;其次,将各波段波形作为状态,窗口样本聚类后的类簇中心作为观测,构建状态转移受限的隐马尔科夫模型;最后,对待预测波群的各波段窗口组合结果进行投票,识别最可能的波群形态。在专业医生标注的真实数据集上,与现存方法比较,SWVHMM F1值分别提高了5.97%,5.49%和2.27%。这表明SWVHMM不仅能识别多种QRS波群形态,而且准确度更高。

The morphological identification of QRS complex is a key in the detection of abnormal ECG,which acts as the basis for disease diagnosis.The existing QRS morphological recognition methods either identify only a few morphologies,or are sensitive to parameter settings,and the performance is not ideal.Based on this,a sliding window voting strategy based on hidden Markov model(SWVHMM)is proposed to automatically identify QRS morphologies.Firstly,each QRS complex is divided into four phases,and a sliding window is set for each phase to extract samples.Secondly,the waveform of each phase is regarded as a state,and the cluster center of the window samples acts as the observation to construct a state-constrained Hidden Marko model.Finally,we vote on the result of the combination of different phase windows to identify the target morphology pattern with the largest possibility.On the real data set labelled by professional doctors,compared with existing methods,our method improves F1 measure by 5.97%,5.49%and 2.27%,respectively.The results show that SWVHMM can identify a variety of morphology patterns with improved accuracy.