Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 13; p. 6180
• Main Authors: Thakur, Smriti, Chao, Paul C-P, Tsai, Cheng-Han
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-07-2023; MDPI
• Subjects: Accelerometers; Accuracy; Algorithms; Axis movements; Blood pressure; Blood vessels; Cardiology; Estimation; Hand (anatomy); Hankel matrices; Hankel matrix; Heart rate; heart rate (HR); Light emitting diodes; Machine learning; motion artifact; notch filter; photoplethysmogram (PPG); Quality assessment; Sensors; Signal processing; singular value decomposition (SVD); Three axis; Veins & arteries; Wavelet transforms; Wearable computers; Wearable technology; Hankel matrix; beats per minutes (bpm); motion artifact; notch filter; photoplethysmogram (PPG); heart rate (HR); singular value decomposition (SVD)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23136180

A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects' hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal (MAR) via Hankel decomposition. PPGs and accelerations were collected using a wearable device equipped with a PPG sensor patch and a 3-axis accelerometer. The motion artifacts caused by hand movements and walking were effectively mitigated by the two aforementioned sub-algorithms. The first sub-algorithm utilized a new quality-assessment criterion to identify highly noise-contaminated PPG signals and exclude them from subsequent processing. The second sub-algorithm employed the Hankel matrix and singular value decomposition (SVD) to effectively identify, decompose, and remove motion artifacts. Experimental data collected during hand-moving and walking were considered for evaluation. The performance of the proposed algorithms was assessed using the datasets from the IEEE Signal Processing Cup 2015. The obtained results demonstrated an average error of merely 0.7345 ± 8.1129 beats per minute (bpm) and a mean absolute error of 1.86 bpm for walking, making it the second most accurate method to date that employs a single PPG and a 3-axis accelerometer. The proposed method also achieved the best accuracy of 3.78 bpm in mean absolute errors among all previously reported studies for hand-moving scenarios.