A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

Autor: Hadri Hussain, Fuad Noman, Sh-Hussain Salleh, Hernando Ombao, S. Balqis Samdin, Chee-Ming Ting
Rok vydání: 2020
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
0206 medical engineering
02 engineering and technology
Viterbi algorithm
Statistics - Applications
Machine Learning (cs.LG)
symbols.namesake
Health Information Management
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
Cluster Analysis
Humans
Applications (stat.AP)
Segmentation
Electrical Engineering and Systems Science - Signal Processing
Electrical and Electronic Engineering
Hidden Markov model
Markov chain
Noise measurement
business.industry
Probabilistic logic
Signal Processing
Computer-Assisted
Pattern recognition
020601 biomedical engineering
Markov Chains
Computer Science Applications
Heart Sounds
Autoregressive model
symbols
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Decoding methods
Heart Auscultation
Biotechnology
Zdroj: IEEE Journal of Biomedical and Health Informatics. 24:705-716
ISSN: 2168-2208
2168-2194
Popis: Objective: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state-of-the-art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs), which, however, are limited by its observation independence assumption and rely on pre-extraction of noise-robust features. Methods: We propose a Markov-switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state. To enhance robustness, we extend the MSAR model to a switching linear dynamic system (SLDS) that jointly model both the switching AR dynamics of underlying heart sound signals and the noise effects. We introduce a novel algorithm via fusion of switching Kalman filter and the duration-dependent Viterbi algorithm, which incorporates the duration of heart sound states to improve state decoding. Results: Evaluated on Physionet/CinC Challenge 2016 dataset, the proposed MSAR-SLDS approach significantly outperforms the hidden semi-Markov model (HSMM) in heart sound segmentation based on raw signals and comparable to a feature-based HSMM. The segmented labels were then used to train Gaussian-mixture HMM classifier for identification of abnormal beats, achieving high average precision of 86.1% on the same dataset including very noisy recordings. Conclusion: The proposed approach shows noticeable performance in heart sound segmentation and classification on a large noisy dataset. Significance: It is potentially useful in developing automated heart monitoring systems for pre-screening of heart pathologies.
Databáze: OpenAIRE
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