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Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Ennio Idrobo-Avila, Humberto Loaiza-Correa, Rubiel Vargas-Canas, Flavio Munoz-Bolanos and Leo Van Noorden (UGent)
(2021) HELIYON. 7(2).
Author
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Abstract
The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.
Keywords
FEATURES, INDEXES, TRENDS, ECG signal classification, Heart rate variability, PhysioNet, physiological signals database, Music, Neural networks

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MLA
Idrobo-Avila, Ennio, et al. “Can the Application of Certain Music Information Retrieval Methods Contribute to the Machine Learning Classification of Electrocardiographic Signals?” HELIYON, vol. 7, no. 2, 2021, doi:10.1016/j.heliyon.2021.e06257.
APA
Idrobo-Avila, E., Loaiza-Correa, H., Vargas-Canas, R., Munoz-Bolanos, F., & Van Noorden, L. (2021). Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? HELIYON, 7(2). https://doi.org/10.1016/j.heliyon.2021.e06257
Chicago author-date
Idrobo-Avila, Ennio, Humberto Loaiza-Correa, Rubiel Vargas-Canas, Flavio Munoz-Bolanos, and Leo Van Noorden. 2021. “Can the Application of Certain Music Information Retrieval Methods Contribute to the Machine Learning Classification of Electrocardiographic Signals?” HELIYON 7 (2). https://doi.org/10.1016/j.heliyon.2021.e06257.
Chicago author-date (all authors)
Idrobo-Avila, Ennio, Humberto Loaiza-Correa, Rubiel Vargas-Canas, Flavio Munoz-Bolanos, and Leo Van Noorden. 2021. “Can the Application of Certain Music Information Retrieval Methods Contribute to the Machine Learning Classification of Electrocardiographic Signals?” HELIYON 7 (2). doi:10.1016/j.heliyon.2021.e06257.
Vancouver
1.
Idrobo-Avila E, Loaiza-Correa H, Vargas-Canas R, Munoz-Bolanos F, Van Noorden L. Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? HELIYON. 2021;7(2).
IEEE
[1]
E. Idrobo-Avila, H. Loaiza-Correa, R. Vargas-Canas, F. Munoz-Bolanos, and L. Van Noorden, “Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?,” HELIYON, vol. 7, no. 2, 2021.
@article{8739923,
  abstract     = {{The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.}},
  articleno    = {{e06257}},
  author       = {{Idrobo-Avila, Ennio and Loaiza-Correa, Humberto and Vargas-Canas, Rubiel and Munoz-Bolanos, Flavio and Van Noorden, Leo}},
  issn         = {{2405-8440}},
  journal      = {{HELIYON}},
  keywords     = {{FEATURES,INDEXES,TRENDS,ECG signal classification,Heart rate variability,PhysioNet,physiological signals database,Music,Neural networks}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{10}},
  title        = {{Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?}},
  url          = {{http://doi.org/10.1016/j.heliyon.2021.e06257}},
  volume       = {{7}},
  year         = {{2021}},
}
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