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Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

  • Image & Signal Processing
  • Published:
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Abstract

Cardiovascular disease is the leading cause of death in the world, and its early detection is a key to improving long-term health outcomes. The auscultation of the heart is still an important method in the medical process because it is very simple and cheap. To detect possible heart anomalies at an early stage, an automatic method enabling cardiac health low-cost screening for the general population would be highly valuable. By analyzing the phonocardiogram signals, it is possible to perform cardiac diagnosis and find possible anomalies at an early-term. Therefore, the development of intelligent and automated analysis tools of the phonocardiogram is very relevant. In this work, we use simultaneously collected electrocardiograms and phonocardiograms from the Physionet Challenge database with the main objective of determining whether a phonocardiogram corresponds to a “normal” or “abnormal” physiological state. Our main contribution is the methodological combination of time domain features and frequency domain features of phonocardiogram signals to improve cardiac disease automatic classification. This novel approach is developed using both features. First, the phonocardiogram signals are segmented with an algorithm based on a logistic regression hidden semi-Markov model, which uses electrocardiogram signals as a reference. Then, two groups of features from the time and frequency domain are extracted from the phonocardiogram segments. One group is based on motifs and the other on Mel-frequency cepstral coefficients. After that, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, a binary classifier is applied to both groups of features to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, three classification algorithms are used: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when both time and Mel-frequency cepstral coefficients features are considered using a Support Vector Machines with a radial kernel.

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Acknowledgements

This work is supported by the NanoSTIMA Project: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016 which is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

Funding

This study was funded by the NanoSTIMA Project: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/ NORTE-01-0145-FEDER-000016 which is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

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Authors and Affiliations

  1. INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200 – 465, Porto, Portugal

    Diogo Marcelo Nogueira, Carlos Abreu Ferreira, Elsa Ferreira Gomes & Alípio M. Jorge

  2. Instituto Superior de Engenharia do Porto, Rua Dr. Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    Carlos Abreu Ferreira & Elsa Ferreira Gomes

  3. Faculdade de Ciências da Universidade do Porto, Rua Campo Alegre 1021/1055, 4169-007, Porto, Portugal

    Alípio M. Jorge

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  1. Diogo Marcelo Nogueira
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  2. Carlos Abreu Ferreira
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  3. Elsa Ferreira Gomes
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  4. Alípio M. Jorge
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Correspondence to Diogo Marcelo Nogueira.

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Nogueira, D.M., Ferreira, C.A., Gomes, E.F. et al. Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features. J Med Syst 43, 168 (2019). https://doi.org/10.1007/s10916-019-1286-5

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