Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?
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 process...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844021003625 |
| _version_ | 1818847020446646272 |
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| author | Ennio Idrobo-Ávila Humberto Loaiza-Correa Rubiel Vargas-Cañas Flavio Muñoz-Bolaños Leon van Noorden |
| author_facet | Ennio Idrobo-Ávila Humberto Loaiza-Correa Rubiel Vargas-Cañas Flavio Muñoz-Bolaños Leon van Noorden |
| author_sort | Ennio Idrobo-Ávila |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T05:54:48Z |
| format | Article |
| id | doaj.art-12b1d64d7f7c41be9e0c1c48d0a91873 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-19T05:54:48Z |
| publishDate | 2021-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-12b1d64d7f7c41be9e0c1c48d0a918732022-12-21T20:33:29ZengElsevierHeliyon2405-84402021-02-0172e06257Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?Ennio Idrobo-Ávila0Humberto Loaiza-Correa1Rubiel Vargas-Cañas2Flavio Muñoz-Bolaños3Leon van Noorden4PSI - Percepción y Sistemas Inteligentes, Escuela de Ingeniería Eléctrica y Electrónica, Universidad del Valle, Cali, Colombia; Corresponding author.PSI - Percepción y Sistemas Inteligentes, Escuela de Ingeniería Eléctrica y Electrónica, Universidad del Valle, Cali, ColombiaSIDICO – Sistemas Dinámicos de Instrumentación y Control, Departamento de Física, Universidad del Cauca, Popayán, ColombiaCIFIEX - Ciencias Fisiológicas Experimentales, Departamento de Ciencias Fisiológicas, Universidad del Cauca, Popayán, ColombiaIPEM - Institute for Systematic Musicology, Department of Art, Music and Theatre Sciences, Ghent University, Ghent, BelgiumThe 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.http://www.sciencedirect.com/science/article/pii/S2405844021003625ECG signal classificationHeart rate variabilityPhysioNet physiological signals databaseMusicNeural networks |
| spellingShingle | Ennio Idrobo-Ávila Humberto Loaiza-Correa Rubiel Vargas-Cañas Flavio Muñoz-Bolaños Leon van Noorden Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? Heliyon ECG signal classification Heart rate variability PhysioNet physiological signals database Music Neural networks |
| title | Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? |
| title_full | Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? |
| title_fullStr | Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? |
| title_full_unstemmed | Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? |
| title_short | Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals? |
| title_sort | can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals |
| topic | ECG signal classification Heart rate variability PhysioNet physiological signals database Music Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2405844021003625 |
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