Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis
Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified app...
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/3235 |
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| author | Koichi Fujiwara Shota Miyatani Asuka Goda Miho Miyajima Tetsuo Sasano Manabu Kano |
| author_facet | Koichi Fujiwara Shota Miyatani Asuka Goda Miho Miyajima Tetsuo Sasano Manabu Kano |
| author_sort | Koichi Fujiwara |
| collection | DOAJ |
| description | Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems. |
| first_indexed | 2024-03-10T11:38:46Z |
| format | Article |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:38:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9e8967469dd14332817de476a0403cbf2023-11-21T18:40:40ZengMDPI AGSensors1424-82202021-05-01219323510.3390/s21093235Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability AnalysisKoichi Fujiwara0Shota Miyatani1Asuka Goda2Miho Miyajima3Tetsuo Sasano4Manabu Kano5Department of Material Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, JapanDepartment of Systems Science, Kyoto University, Kyoto 606-8501, JapanDepartment of Systems Science, Kyoto University, Kyoto 606-8501, JapanDepartment of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, JapanDepartment of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, JapanDepartment of Systems Science, Kyoto University, Kyoto 606-8501, JapanHeart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.https://www.mdpi.com/1424-8220/21/9/3235heart rate variability analysisextrasystoleRRI datamachine learningautoencoder |
| spellingShingle | Koichi Fujiwara Shota Miyatani Asuka Goda Miho Miyajima Tetsuo Sasano Manabu Kano Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis Sensors heart rate variability analysis extrasystole RRI data machine learning autoencoder |
| title | Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis |
| title_full | Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis |
| title_fullStr | Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis |
| title_full_unstemmed | Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis |
| title_short | Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis |
| title_sort | autoencoder based extrasystole detection and modification of rri data for precise heart rate variability analysis |
| topic | heart rate variability analysis extrasystole RRI data machine learning autoencoder |
| url | https://www.mdpi.com/1424-8220/21/9/3235 |
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