DeepHeart: Accurate Heart Rate Estimation from PPG Signals Based on Deep Learning

PPG-based heart rate estimation has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user's physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios (e.g., running or biking), making them impractical in real-world settings where a user may perform a wide range of physical activities. In this paper, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from ECG signals based on a training data set. Then a denoising convolutional neural network (DnCNN) is trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by the DnCNN and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup (SPC) training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.98 bpm, outperforming two state-of-the-art methods TROIKA and Deep PPG.

[1]  Houbing Song,et al.  Spectral Matrix Decomposition-Based Motion Artifacts Removal in Multi-Channel PPG Sensor Signals , 2016, IEEE Access.

[2]  Vasu Jindal.  MobileSOFT : U : A Deep Learning Framework to Monitor Heart Rate During Intensive Physical Exercise , 2017 .

[3]  Mario Konijnenburg,et al.  CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment , 2019, IEEE Transactions on Biomedical Circuits and Systems.

[4]  Ki H. Chon,et al.  A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor , 2015, Sensors.

[5]  Jo Woon Chong,et al.  A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time–Frequency Spectral Features , 2017, IEEE Journal of Biomedical and Health Informatics.

[6]  Rosmina Jaafar,et al.  Estimation of breathing rate and heart rate from photoplethysmogram , 2017, 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI).

[7]  Farrokh Marvasti,et al.  Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[8]  Mohammad B. Shamsollahi,et al.  Heart Rate monitoring during physical exercise using wrist-type photoplethysmographic (PPG) signals , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  Jean-Marc Vesin,et al.  Adaptive frequency tracking for robust heart rate estimation using wrist-type photoplethysmographic signals during physical exercise , 2015, 2015 Computing in Cardiology Conference (CinC).

[10]  Sun K. Yoo,et al.  Motion artifact reduction in photoplethysmography using independent component analysis , 2006, IEEE Transactions on Biomedical Engineering.

[11]  Michele Rossi,et al.  A supervised learning approach for the robust detection of heart beat in plethysmographic data , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  Mohammad Tariqul Islam,et al.  An efficient method for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[13]  Md. Kamrul Hasan,et al.  A Robust Heart Rate Monitoring Scheme Using Photoplethysmographic Signals Corrupted by Intense Motion Artifacts , 2016, IEEE Transactions on Biomedical Engineering.

[14]  Michael Muma,et al.  A new method for heart rate monitoring during physical exercise using photoplethysmographic signals , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[15]  Guoliang Xing,et al.  FitBeat: A Lightweight System for Accurate Heart Rate Measurement during Exercise , 2017, 2017 IEEE International Conference on Smart Computing (SMARTCOMP).

[16]  Changseok Bae,et al.  Adaptive Noise Cancellation Using Accelerometers for the PPG Signal from Forehead , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Kristof Van Laerhoven,et al.  Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks , 2019, Sensors.

[18]  Zhilin Zhang,et al.  Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography , 2015, Biomed. Signal Process. Control..

[19]  Farrokh Marvasti,et al.  Low Complexity Heart Rate Measurement from Wearable Wrist-Type Photoplethysmographic Sensors Robust to Motion Artifacts , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Zhilin Zhang,et al.  TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise , 2014, IEEE Transactions on Biomedical Engineering.

[21]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[22]  E. Hari Krishna,et al.  A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter , 2012, IEEE Transactions on Instrumentation and Measurement.

[23]  Zhilin Zhang,et al.  Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction , 2015, IEEE Transactions on Biomedical Engineering.