ECG segmentation algorithm based on bidirectional hidden semi-Markov model
ECG segmentation algorithm based on bidirectional hidden semi-Markov model
复制标题
基于双向隐半马尔可夫模型的心电分割算法
DOI：
10.1016/j.compbiomed.2022.106081
复制
发表时间：
2022-09
影响因子：
7.7
通讯作者：
魏守水
中科院分区：
文献类型：
--
作者：
火蕊;张立亭;刘飞飞;王颖;梁业松;魏守水
心电图波形的准确分割是心血管疾病诊断的关键。提出了一种基于心电波形持续时间概率分布的双向隐半马尔可夫模型（BI-HSMM）用于心电波形分割。提取心电信号的4个特征向量作为隐马尔可夫模型（HMM）的观测序列，统计每个波形持续时间的统计概率分布。Logistic回归（LR）用于训练模型参数。首先检测QRS波的起始和结束位置，然后对其他波进行双向预测。预测了心电图、ST段、T波和TP段。检测背向反射、P波和PQ段。结合前向预测和后向回溯算法的递推公式对Viterbi算法进行了改进。在QT数据库中，该方法表现出优异的性能（Acc= 97.98%，P波F1评分= 98.37%，QRS波F1评分= 97.60%，T波F1评分= 97.79%）。对山东省立医院采集的穿戴式动态心电图信号，检测准确率为99.71%，各波形的F1均在99%以上。实验结果和真实的动态心电图信号验证表明，新的BI-HSMM方法能够有效地分割静息和动态心电图信号，有利于心血管疾病的检测和监测。
Accurate segmentation of electrocardiogram (ECG) waves is crucial for cardiovascular diseases (CVDs). In this study, a bidirectional hidden semi-Markov model (BI-HSMM) based on the probability distributions of ECG waveform duration was proposed for ECG wave segmentation. Four feature-vectors of ECG signals were extracted as the observation sequence of the hidden Markov model (HMM), and the statistical probability distribution of each waveform duration was counted. Logistic regression (LR) was used to train model parameters. The starting and ending positions of the QRS wave were first detected, and thereafter, bidirectional prediction was employed for the other waves. Forwardly, ST segment, T wave, and TP segment were predicted. Backwardly, P wave and PQ segments were detected. The Viterbi algorithm was improved by integrating the recursive formula of the forward prediction and backward backtracking algorithms. In the QT database, the proposed method demonstrated excellent performance (Acc=97.98%, F1 score of P wave = 98.37%, F1 score of QRS wave = 97.60%, F1 score of T wave = 97.79%). For the wearable dynamic electrocardiography (DCG) signals collected by the Shandong Provincial Hospital (SPH), the detection accuracy was 99.71% and the F1 of each waveform was above 99%. The experimental results and real DCG signal validation confirmed that the proposed new BI-HSMM method exhibits significant ability to segment the resting and DCG signals; this is conducive to the detection and monitoring of CVDs.
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DOI：
10.1109/icmla51294.2020.00176
发表时间：
2020-12
期刊：
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
影响因子：
--
作者：
Chandresh Pravin;Varun Ojha
通讯作者：
Chandresh Pravin;Varun Ojha
影响因子：
2
作者：
Fu, Fan;Xiang, Wentao;Li, Jianqing
通讯作者：
Li, Jianqing
影响因子：
1.9
作者：
R. Andreão;J. Boudy
通讯作者：
R. Andreão;J. Boudy
影响因子：
5.1
作者：
Khazaee, A.;Ebrahimzadeh, A.
通讯作者：
Ebrahimzadeh, A.
DOI：
10.22489/cinc.2018.120
发表时间：
2018-12
期刊：
2018 Computing in Cardiology Conference (CinC)
影响因子：
--
作者：
Borja Altamira;E. Alonso;U. Irusta;E. Aramendi;M. Daya
通讯作者：
Borja Altamira;E. Alonso;U. Irusta;E. Aramendi;M. Daya