SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals
Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/3/506 |
| _version_ | 1828115456849870848 |
|---|---|
| author | Jiping Xiong Lisang Cai Fei Wang Xiaowei He |
| author_facet | Jiping Xiong Lisang Cai Fei Wang Xiaowei He |
| author_sort | Jiping Xiong |
| collection | DOAJ |
| description | Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise. |
| first_indexed | 2024-04-11T12:40:07Z |
| format | Article |
| id | doaj.art-116693d400fa4ce2b67fc48c74ebae64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:40:07Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-116693d400fa4ce2b67fc48c74ebae642022-12-22T04:23:31ZengMDPI AGSensors1424-82202017-03-0117350610.3390/s17030506s17030506SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor SignalsJiping Xiong0Lisang Cai1Fei Wang2Xiaowei He3College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaAlthough wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.http://www.mdpi.com/1424-8220/17/3/506adaptive filtercompressive sensingheart rate estimationwrist-type photoplethysmography (WPPG)principle component analysis (PCA)support vector machine (SVM) |
| spellingShingle | Jiping Xiong Lisang Cai Fei Wang Xiaowei He SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals Sensors adaptive filter compressive sensing heart rate estimation wrist-type photoplethysmography (WPPG) principle component analysis (PCA) support vector machine (SVM) |
| title | SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals |
| title_full | SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals |
| title_fullStr | SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals |
| title_full_unstemmed | SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals |
| title_short | SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals |
| title_sort | svm based spectral analysis for heart rate from multi channel wppg sensor signals |
| topic | adaptive filter compressive sensing heart rate estimation wrist-type photoplethysmography (WPPG) principle component analysis (PCA) support vector machine (SVM) |
| url | http://www.mdpi.com/1424-8220/17/3/506 |
| work_keys_str_mv | AT jipingxiong svmbasedspectralanalysisforheartratefrommultichannelwppgsensorsignals AT lisangcai svmbasedspectralanalysisforheartratefrommultichannelwppgsensorsignals AT feiwang svmbasedspectralanalysisforheartratefrommultichannelwppgsensorsignals AT xiaoweihe svmbasedspectralanalysisforheartratefrommultichannelwppgsensorsignals |