Using neural network with random weights and mutual information for systolic peaks classification of PPG signals
The detection of peaks in photoplethysmogram (PPG) signals is important to ensure the information gather from the peaks in accurate manner. The false peaks will interrupt the accuracy for future classification of any related events. This study presents the implementation of feature enhancement metho...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Association for Computing Machinery, New York, United States
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28429/ http://umpir.ump.edu.my/id/eprint/28429/1/50.%20Using%20neural%20network%20with%20random%20weights%20and%20mutual%20information.pdf http://umpir.ump.edu.my/id/eprint/28429/2/50.1%20Using%20neural%20network%20with%20random%20weights%20and%20mutual%20information.pdf |
| _version_ | 1848823051305615360 |
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| author | Muhammad Haziq, Mohd Rasid Noor Liza, Simon Asrul, Adam |
| author_facet | Muhammad Haziq, Mohd Rasid Noor Liza, Simon Asrul, Adam |
| author_sort | Muhammad Haziq, Mohd Rasid |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The detection of peaks in photoplethysmogram (PPG) signals is important to ensure the information gather from the peaks in accurate manner. The false peaks will interrupt the accuracy for future classification of any related events. This study presents the implementation of feature enhancement method for systolic peaks classification of PPG signals using mutual information and neural network with random weights (MI-NNRW). MI-NNRW method is proposed to improve the accuracy performance of NNRW method. Ml method implements at sixteen time-domain features and then NNRW classifier predicts between false and true systolic peaks point of PPG signals. The results indicate that by using sigmoid as activation function, the accuracy of sensitivity (Se) for ICP signals increase up to 81.71 percent. Overall, MI-NNRW method improves the accuracy performance compared to NNRW method which is leads to the improvement of accuracy for detection of systolic peaks. |
| first_indexed | 2025-11-15T02:50:59Z |
| format | Conference or Workshop Item |
| id | ump-28429 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:50:59Z |
| publishDate | 2020 |
| publisher | Association for Computing Machinery, New York, United States |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-284292021-01-26T01:35:53Z http://umpir.ump.edu.my/id/eprint/28429/ Using neural network with random weights and mutual information for systolic peaks classification of PPG signals Muhammad Haziq, Mohd Rasid Noor Liza, Simon Asrul, Adam TS Manufactures The detection of peaks in photoplethysmogram (PPG) signals is important to ensure the information gather from the peaks in accurate manner. The false peaks will interrupt the accuracy for future classification of any related events. This study presents the implementation of feature enhancement method for systolic peaks classification of PPG signals using mutual information and neural network with random weights (MI-NNRW). MI-NNRW method is proposed to improve the accuracy performance of NNRW method. Ml method implements at sixteen time-domain features and then NNRW classifier predicts between false and true systolic peaks point of PPG signals. The results indicate that by using sigmoid as activation function, the accuracy of sensitivity (Se) for ICP signals increase up to 81.71 percent. Overall, MI-NNRW method improves the accuracy performance compared to NNRW method which is leads to the improvement of accuracy for detection of systolic peaks. Association for Computing Machinery, New York, United States 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28429/1/50.%20Using%20neural%20network%20with%20random%20weights%20and%20mutual%20information.pdf pdf en http://umpir.ump.edu.my/id/eprint/28429/2/50.1%20Using%20neural%20network%20with%20random%20weights%20and%20mutual%20information.pdf Muhammad Haziq, Mohd Rasid and Noor Liza, Simon and Asrul, Adam (2020) Using neural network with random weights and mutual information for systolic peaks classification of PPG signals. In: ICBET 2020: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology , September 2020 , Tokyo, Japan. pp. 276-283.. ISBN 978-1-4503-7724-9 (Published) https://doi.org/10.1145/3397391.3397394 |
| spellingShingle | TS Manufactures Muhammad Haziq, Mohd Rasid Noor Liza, Simon Asrul, Adam Using neural network with random weights and mutual information for systolic peaks classification of PPG signals |
| title | Using neural network with random weights and mutual information for systolic peaks classification of PPG signals |
| title_full | Using neural network with random weights and mutual information for systolic peaks classification of PPG signals |
| title_fullStr | Using neural network with random weights and mutual information for systolic peaks classification of PPG signals |
| title_full_unstemmed | Using neural network with random weights and mutual information for systolic peaks classification of PPG signals |
| title_short | Using neural network with random weights and mutual information for systolic peaks classification of PPG signals |
| title_sort | using neural network with random weights and mutual information for systolic peaks classification of ppg signals |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/28429/ http://umpir.ump.edu.my/id/eprint/28429/ http://umpir.ump.edu.my/id/eprint/28429/1/50.%20Using%20neural%20network%20with%20random%20weights%20and%20mutual%20information.pdf http://umpir.ump.edu.my/id/eprint/28429/2/50.1%20Using%20neural%20network%20with%20random%20weights%20and%20mutual%20information.pdf |