Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation
Photoplethysmogram (PPG) signals contain valuable health information that is in the relation between the volumetric variations of blood circulation and the cardiovascular and respiratory systems. This study introduces the performance evaluation on open clinical benchmark PPG signals with a multiclas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37222/1/Multiclass%20classification%20of%20systolic%20and%20diastolic%20peaks.pdf |
| _version_ | 1825814890009329664 |
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| author | Simon, Noor liza Adam, Asrul Low, Chen Yik Ibrahim, Zuwairie Shapia, Mohd Ibrahim |
| author_facet | Simon, Noor liza Adam, Asrul Low, Chen Yik Ibrahim, Zuwairie Shapia, Mohd Ibrahim |
| author_sort | Simon, Noor liza |
| collection | UMP |
| description | Photoplethysmogram (PPG) signals contain valuable health information that is in the relation between the volumetric variations of blood circulation and the cardiovascular and respiratory systems. This study introduces the performance evaluation on open clinical benchmark PPG signals with a multiclass neural network with random weights (NNRW) classification method for systolic peak and diastolic point detection. The best performance of the peak and point detection is crucial to be achieved at the early stage for extracting further valuable information in addition to future predictions of cardiovascular-related illness. Various open clinical datasets of PPG signals have been introduced, however, there is a lack of information on peak annotations. Due to the lack of peak annotation information, it is time-consuming to be prepared. One suitable clinical benchmark dataset with peak annotation information for peak detection has been previously evaluated, however, it cannot be generalized and rely upon only one dataset. Therefore, for generalization, there is a new open clinical benchmark dataset that is found in the year 2018 and our own collected data from normal participants is utilized in this study. The findings exhibit more convincing overall accuracy and Gmean of testing results with 94.86 and 94.74 percent, respectively. The findings of the comparison with previous work indicate that the proposed methodology to predict PPG-based multi-class systolic and diastolic points is more generalizable. |
| first_indexed | 2024-03-06T13:05:16Z |
| format | Article |
| id | UMPir37222 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:05:16Z |
| publishDate | 2022 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir372222023-03-08T08:03:02Z http://umpir.ump.edu.my/id/eprint/37222/ Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation Simon, Noor liza Adam, Asrul Low, Chen Yik Ibrahim, Zuwairie Shapia, Mohd Ibrahim RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering Photoplethysmogram (PPG) signals contain valuable health information that is in the relation between the volumetric variations of blood circulation and the cardiovascular and respiratory systems. This study introduces the performance evaluation on open clinical benchmark PPG signals with a multiclass neural network with random weights (NNRW) classification method for systolic peak and diastolic point detection. The best performance of the peak and point detection is crucial to be achieved at the early stage for extracting further valuable information in addition to future predictions of cardiovascular-related illness. Various open clinical datasets of PPG signals have been introduced, however, there is a lack of information on peak annotations. Due to the lack of peak annotation information, it is time-consuming to be prepared. One suitable clinical benchmark dataset with peak annotation information for peak detection has been previously evaluated, however, it cannot be generalized and rely upon only one dataset. Therefore, for generalization, there is a new open clinical benchmark dataset that is found in the year 2018 and our own collected data from normal participants is utilized in this study. The findings exhibit more convincing overall accuracy and Gmean of testing results with 94.86 and 94.74 percent, respectively. The findings of the comparison with previous work indicate that the proposed methodology to predict PPG-based multi-class systolic and diastolic points is more generalizable. Penerbit UMP 2022 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/37222/1/Multiclass%20classification%20of%20systolic%20and%20diastolic%20peaks.pdf Simon, Noor liza and Adam, Asrul and Low, Chen Yik and Ibrahim, Zuwairie and Shapia, Mohd Ibrahim (2022) Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 4 (1). pp. 56-69. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v4i1.7758 https://doi.org/10.15282/mekatronika.v4i1.7758 |
| spellingShingle | RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering Simon, Noor liza Adam, Asrul Low, Chen Yik Ibrahim, Zuwairie Shapia, Mohd Ibrahim Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation |
| title | Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation |
| title_full | Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation |
| title_fullStr | Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation |
| title_full_unstemmed | Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation |
| title_short | Multiclass classification of systolic and diastolic peaks on open benchmark PPG signals: Performance evaluation |
| title_sort | multiclass classification of systolic and diastolic peaks on open benchmark ppg signals performance evaluation |
| topic | RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37222/1/Multiclass%20classification%20of%20systolic%20and%20diastolic%20peaks.pdf |
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