COUPLED HIDDEN MARKOV MODEL FOR AUTOMATIC ECG AND PCG SEGMENTATION

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Oliveira, J (Autor) Outra; Sousa, C (Autor) Outra; Coimbra, M (Autor) FCUP

Ata de Conferência Internacional

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

Páginas: 1023-1027

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

New Orleans, LA, MAR 05-09, 2017

Indexação

ISI Web of Knowledge - 5 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5