Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging
Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECG...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2693 |
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| author | Ahmad Suliman Md Rakibul Mowla Alaleh Alivar Charles Carlson Punit Prakash Balasubramaniam Natarajan Steve Warren David E. Thompson |
| author_facet | Ahmad Suliman Md Rakibul Mowla Alaleh Alivar Charles Carlson Punit Prakash Balasubramaniam Natarajan Steve Warren David E. Thompson |
| author_sort | Ahmad Suliman |
| collection | DOAJ |
| description | Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined. |
| first_indexed | 2024-03-11T07:10:46Z |
| format | Article |
| id | doaj.art-b7a58750435e4de3bafa1d320dfc769f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:46Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b7a58750435e4de3bafa1d320dfc769f2023-11-17T08:38:22ZengMDPI AGSensors1424-82202023-03-01235269310.3390/s23052693Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep StagingAhmad Suliman0Md Rakibul Mowla1Alaleh Alivar2Charles Carlson3Punit Prakash4Balasubramaniam Natarajan5Steve Warren6David E. Thompson7Mike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAMike Wiegers Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USAHeart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.https://www.mdpi.com/1424-8220/23/5/2693ballistocardiogramBayes errorclassifierelectrocardiogramheartbeat intervalk-nearest neighbor |
| spellingShingle | Ahmad Suliman Md Rakibul Mowla Alaleh Alivar Charles Carlson Punit Prakash Balasubramaniam Natarajan Steve Warren David E. Thompson Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging Sensors ballistocardiogram Bayes error classifier electrocardiogram heartbeat interval k-nearest neighbor |
| title | Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging |
| title_full | Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging |
| title_fullStr | Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging |
| title_full_unstemmed | Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging |
| title_short | Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging |
| title_sort | effects of ballistocardiogram peak detection jitters on the quality of heart rate variability features a simulation based case study in the context of sleep staging |
| topic | ballistocardiogram Bayes error classifier electrocardiogram heartbeat interval k-nearest neighbor |
| url | https://www.mdpi.com/1424-8220/23/5/2693 |
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