Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitorin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8796 |
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| author | Ramón A. Félix Alberto Ochoa-Brust Walter Mata-López Rafael Martínez-Peláez Luis J. Mena Laura L. Valdez-Velázquez |
| author_facet | Ramón A. Félix Alberto Ochoa-Brust Walter Mata-López Rafael Martínez-Peláez Luis J. Mena Laura L. Valdez-Velázquez |
| author_sort | Ramón A. Félix |
| collection | DOAJ |
| description | Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts. |
| first_indexed | 2024-03-11T11:21:41Z |
| format | Article |
| id | doaj.art-c017d4af81754df0996cc005227c022d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:41Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c017d4af81754df0996cc005227c022d2023-11-10T15:12:07ZengMDPI AGSensors1424-82202023-10-012321879610.3390/s23218796Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG DevicesRamón A. Félix0Alberto Ochoa-Brust1Walter Mata-López2Rafael Martínez-Peláez3Luis J. Mena4Laura L. Valdez-Velázquez5Facultad de Ingeniería Mecánica y Eléctrica, Universidad de Colima, Colima 28400, MexicoFacultad de Ingeniería Mecánica y Eléctrica, Universidad de Colima, Colima 28400, MexicoFacultad de Ingeniería Mecánica y Eléctrica, Universidad de Colima, Colima 28400, MexicoDepartamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Antofagasta 1249004, ChileUnidad Académica de Computación, Universidad Politécnica de Sinaloa, Mazatlán 82199, MexicoFacultad de Ciencias Químicas, Universidad de Colima, Colima 28400, MexicoHeart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.https://www.mdpi.com/1424-8220/23/21/8796R-peak detectionfast parabolic fittingwearable ECG devices |
| spellingShingle | Ramón A. Félix Alberto Ochoa-Brust Walter Mata-López Rafael Martínez-Peláez Luis J. Mena Laura L. Valdez-Velázquez Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices Sensors R-peak detection fast parabolic fitting wearable ECG devices |
| title | Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices |
| title_full | Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices |
| title_fullStr | Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices |
| title_full_unstemmed | Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices |
| title_short | Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices |
| title_sort | fast parabolic fitting an r peak detection algorithm for wearable ecg devices |
| topic | R-peak detection fast parabolic fitting wearable ECG devices |
| url | https://www.mdpi.com/1424-8220/23/21/8796 |
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