Optimal Preprocessing of Raw Signals from Reflective Mode Photoplethysmography in Wearable Devices

Abstract

The optical measurement principle photoplethysmography has emerged in today's wearable devices as the standard to monitor the wearer's heart rate in everyday life. This cost-effective and easy-to-integrate technique has transformed from the original transmission mode pulse oximetry for clinical settings to the reflective mode of modern ambulatory, wrist-worn devices. Numerous proposed algorithms aim at the efficient heart rate measurement and accurate detection of the consecutive pulses for the derivation of secondary features from the heart rate variability. Most, however, have been evaluated either on own, closed recordings or on public datasets that often stem from clinical pulse oximeters in transmission instead of wearables' reflective mode. Signals tend furthermore to be preprocessed with filters, which are rarely documented and unintentionally fitted to the available and applied signals.We investigate the influence of preprocessing on the peak positions and present the benchmark of two cutting-edge pulse detection algorithms on actual raw measurements from reflective mode photoplethysmography. Based on 21806 pulse labels, our evaluation shows that the most suitable but still universal filter passband is located at 0.5 to 15.0 Hz since it preserves the required harmonics to shape the peak positions.

Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

“…Performance Analysis of Proposed Classifier. This study's datasets are acquired from a WESAD Database [37]. These datasets are attained from various stress environments, which recorded 28 people's ECG stress data, 15 males and 13 females.…”

Photoplethysmography-Based Distance Estimation for True Wireless Stereo

“…On the one hand, in addition to studies that employ the raw PPG data [ 7 ], studies that reduce the instability of the raw PPG data by applying several filters have been conducted [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Multi-mode particle filtering methods that demonstrate the performance improvement of an average error of less than 2 BPM compared to single-mode particle filtering and advanced methods with approximately 47 PPG recordings were introduced [ 23 ].…”

“…Multi-mode particle filtering methods that demonstrate the performance improvement of an average error of less than 2 BPM compared to single-mode particle filtering and advanced methods with approximately 47 PPG recordings were introduced [ 23 ]. Two cutting-edge pulse detection algorithms on actual raw PPG data were studied [ 24 ]. This work demonstrated the effect of preprocessing pulse peak positions and the performance of peak detection algorithm was analyzed on 21,806 pulse data [ 25 ].…”

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

“…Movement is the most common source of artifacts, called motion artifacts (MAs) [ 1 ]. To mitigate these issues, typical PPG denoising methods involve band-pass filtering (0.5 to 15 Hz) to preserve heart rate frequency (0.83 to 3.33 Hz) while removing baseline fluctuations due to respiration (0.13 to 0.67 Hz) [ 13 ]. Notwithstanding, wavelet decomposition or comb filtering might be more efficient preprocessing methods leading to a higher signal-to-noise ratio (SNR) [ 14 , 15 ].…”

“…Notwithstanding, wavelet decomposition or comb filtering might be more efficient preprocessing methods leading to a higher signal-to-noise ratio (SNR) [ 14 , 15 ]. It is worth noting that the frequency spectrum of MAs can overlap with HR frequencies, necessitating additional processing for their detection and removal, which remains a challenging task [ 4 , 5 , 7 , 12 , 13 , 16 ].…”