COUPLED HIDDEN MARKOV MODEL FOR AUTOMATIC ECG AND PCG SEGMENTATION

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Oliveira, J (Author) Other; Sousa, C (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

Pages: 1023-1027

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

New Orleans, LA, MAR 05-09, 2017

Indexing

ISI Web of Knowledge - 5 Citations

Other information

Authenticus ID: P-00M-YKP

DOI: 10.1109/icassp.2017.7952311

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5