Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms
Heart rate variability (HRV) is an important marker in various cardiovascular and non-cardiovascular conditions. This study aimed to evaluate the effectiveness of three linear models (Logistic Regression, Ridge Regression, Support Vector Machine) in distinguishing between healthy individuals and tho...
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| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Current Directions in Biomedical Engineering |
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| Online Access: | https://doi.org/10.1515/cdbme-2024-2144 |
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| author | Siecinski Szymon Doniec Rafal Jan Grzegorzek Marcin |
| author_facet | Siecinski Szymon Doniec Rafal Jan Grzegorzek Marcin |
| author_sort | Siecinski Szymon |
| collection | DOAJ |
| description | Heart rate variability (HRV) is an important marker in various cardiovascular and non-cardiovascular conditions. This study aimed to evaluate the effectiveness of three linear models (Logistic Regression, Ridge Regression, Support Vector Machine) in distinguishing between healthy individuals and those with valvular heart diseases (VHD) using time domain and frequency domain HRV indices derived from electrocardiographic (ECG) signals. We analyzed 59 recordings taken from two public datasets containing electrocardiographic, seismocardiographic, and gyrocardiographic signals from “Mechanocardiograms with ECG reference” and “An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases” that contain 29 and 30 recordings, respectively. HRV analysis included time and frequency domain indices and the linear models were evaluated using 5-fold stratified cross-validation. The highest sensitivity, PPV, accuracy and F1 score were observed for Logistic Regression (0.8810, 0.8819, 0.8814, 0.8812), followed by Ridge Regression (0.8805, 0.8858, 0.8814, 0.8808), and the lowest were observed for linear SVM (0.8310, 0.8318, 0.8305, 0.8305). The results showed that it is possible to distinguish healthy volunteers and patients with linear classifiers and time domain and frequency domain HRV indices obtained from ECG signals with decent performance. |
| format | Article |
| id | doaj-art-c9e5dfdcf1af4bf59379ff72155c0b1d |
| institution | DOAJ |
| issn | 2364-5504 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj-art-c9e5dfdcf1af4bf59379ff72155c0b1d2025-08-20T02:58:46ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042024-12-0110458759010.1515/cdbme-2024-2144Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiogramsSiecinski Szymon0Doniec Rafal Jan1Grzegorzek Marcin2Institute of Medical Informatics, University of Lubeck, Ratzeburger Allee 160,Lubeck, GermanyInstitute of Medical Informatics, University of Lubeck, Ratzeburger Allee 160,Lubeck, GermanyInstitute of Medical Informatics, University of Lubeck, Ratzeburger Allee 160,Lubeck, GermanyHeart rate variability (HRV) is an important marker in various cardiovascular and non-cardiovascular conditions. This study aimed to evaluate the effectiveness of three linear models (Logistic Regression, Ridge Regression, Support Vector Machine) in distinguishing between healthy individuals and those with valvular heart diseases (VHD) using time domain and frequency domain HRV indices derived from electrocardiographic (ECG) signals. We analyzed 59 recordings taken from two public datasets containing electrocardiographic, seismocardiographic, and gyrocardiographic signals from “Mechanocardiograms with ECG reference” and “An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases” that contain 29 and 30 recordings, respectively. HRV analysis included time and frequency domain indices and the linear models were evaluated using 5-fold stratified cross-validation. The highest sensitivity, PPV, accuracy and F1 score were observed for Logistic Regression (0.8810, 0.8819, 0.8814, 0.8812), followed by Ridge Regression (0.8805, 0.8858, 0.8814, 0.8808), and the lowest were observed for linear SVM (0.8310, 0.8318, 0.8305, 0.8305). The results showed that it is possible to distinguish healthy volunteers and patients with linear classifiers and time domain and frequency domain HRV indices obtained from ECG signals with decent performance.https://doi.org/10.1515/cdbme-2024-2144heart rate variabilityelectrocardiographyvalvular heart diseaselinear classifiers |
| spellingShingle | Siecinski Szymon Doniec Rafal Jan Grzegorzek Marcin Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms Current Directions in Biomedical Engineering heart rate variability electrocardiography valvular heart disease linear classifiers |
| title | Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms |
| title_full | Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms |
| title_fullStr | Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms |
| title_full_unstemmed | Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms |
| title_short | Linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms |
| title_sort | linear classification of healthy people and patients with valvular heart diseases based on heart rate variability indices derived from electrocardiograms |
| topic | heart rate variability electrocardiography valvular heart disease linear classifiers |
| url | https://doi.org/10.1515/cdbme-2024-2144 |
| work_keys_str_mv | AT siecinskiszymon linearclassificationofhealthypeopleandpatientswithvalvularheartdiseasesbasedonheartratevariabilityindicesderivedfromelectrocardiograms AT doniecrafaljan linearclassificationofhealthypeopleandpatientswithvalvularheartdiseasesbasedonheartratevariabilityindicesderivedfromelectrocardiograms AT grzegorzekmarcin linearclassificationofhealthypeopleandpatientswithvalvularheartdiseasesbasedonheartratevariabilityindicesderivedfromelectrocardiograms |