Application of Neural Networks for Heart Rate Monitoring. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Askari, Mohammad Reza; Hajizadeh, Iman; Sevil, Mert; Rashid, Mudassir; Hobbs, Nichole; Brandt, Rachel; Sun, Xiaoyu; Cinar, Ali
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 16161
• Page End: 16166
• Publication Date: 2020
• Subjects: Developments in measurement -- Filtering -- smoothing -- Heart rate estimation -- Neural networks -- Signal processing; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.605
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper addresses the problem of heart rate (HR) monitoring from photo-plethysmography(PPG) sensors, where artifacts caused by body movements drastically affect the quality of the measurement signal. The PPG signal is windowed into consecutive segments, and for each time-windows, a Butterworth bandpass filter is utilized to attenuate high-frequency noises. Then, the PPG signal is processed by using the singular spectrum analysis technique to obtain a smooth PPG signal. In order to remove artifacts caused by the physical activity of the subject, the 3-dimensional accelerometer signal is used as an auxiliary signal to detect the presence of motion artifact (MA). A new spectral subtraction approach is proposed for MA rejection. For the purpose of HR estimation from the PPG signal, a feature extraction method is performed, and neural network binary classifier is used to detect the most probable frequencies corresponding to the actual HR. HR estimations are passed through a Kalman filter to result in smooth and accurate HR estimations.