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Using neural network with random weights and mutual information for systolic peaks classification of PPG signals

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

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Abstract

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.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Systolic peaks classification, Neural network with random weights, Mutual information, PPG signals
Subjects: T Technology > TS Manufactures
Faculty/Division: Institute of Postgraduate Studies
Faculty of Mechanical & Manufacturing Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 26 Jan 2021 01:35
Last Modified: 26 Jan 2021 01:35
URI: http://umpir.ump.edu.my/id/eprint/28429
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