COUPLED HIDDEN MARKOV MODEL FOR AUTOMATIC ECG AND PCG SEGMENTATION

被引:0
|
作者
Oliveira, Jorge [1 ]
Sousa, Catarina [2 ]
Coimbra, Miguel. T. [1 ]
机构
[1] Univ Porto, Fac Sci, Inst Telecomunicacoes, Dept Comp Sci, Oporto, Portugal
[2] Univ Porto, Fac Sci, Fac Med, Oporto, Portugal
关键词
Coupled Hidden Markov Models (CHMM); Hidden Markov Models (HMM); Phonocardiogram (PCG); Electrocardiogram (ECG); Heart Sounds;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Automatic and simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) segmentation is a good example of current challenges when designing multi-channel decision support systems for healthcare. In this paper, we implemented and tested a Montazeri coupled hidden Markov model (CHMM), where two HMM's cooperate to recreate the "true" state sequence. To evaluate its performance, we tested different settings (two fully connected and two partially connected channels) on a real dataset annotated by an expert. The fully connected model achieved 71% of positive predictability (P+) on the ECG channel and 67% of P+ on the PCG channel. The partially connected model achieved 90% of P+ on the ECG channel and 80% of P+ in the PCG channel. These results validate the potential of our approach for real world multichannel application systems.
引用
收藏
页码:1023 / 1027
页数:5
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