Detection and Removal of Motion Artifacts in PPG Signals

Abstract

With the rise of wearable devices, which integrate myriad of health-care and fitness procedures into daily life, a reliable method for measuring various bio-signals in a daily setup is more desired than ever. Many of these physiological parameters, such as Heart rate (HR) and Respiratory Rate (RR), are extracted indirectly and using other signals such as Photoplethysmograph (PPG). Part of the reason is that in some cases, such as RR measurements, the devices which directly measure them are cumbersome to wear and thus, rather impractical. On the other hand, signals, such as PPG from which the RR can be extracted, are not very clean. This poses a challenge on reliable extraction of these metrics. The most important problem is that they are corrupted by motion artifacts. In this paper, we review the state of the art algorithms which are used to detect and filter motion artifacts in PPG signals and compare them in terms of their performance. The insight provided by this paper can help the scientists and engineers to obtain a better understanding of the field and be able to use the most suitable technique for their work, or come up with innovative solutions based on existing ones.

Dynamic tracking of microvascular hemoglobin content for continuous perfusion monitoring in the intensive care unit: pilot feasibility study

“…Moreover, MHC signal quality was high, with a large majority of recorded data suitable for analysis. Motion artifacts were notable, but can been minimized in the future, as with other continuous physiological waveforms including pulse oximetry [28], cardiac telemetry [29], and arterial Fig. 3 Signal power composition for each patient (n = 31) demonstrates the percentage contribution (%Power) from each frequency band relative to overall signal power (B2 endothelial, B3 neurogenic, B4 myogenic, B5 respiratory).…”

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

“…Table 3 shows the details of the sensors in each set. All continuously recording sensors 9 were connected to an ATMEGA328P microcontroller which reads the sensors values with a sampling frequency of 50 Hz. Finally, an Android phone, connected to this microcontroller via a USB-to-Serial converter, recorded the data.…”

“…8 As shown in Table 3, the MAX30100 PPG sensor was used as accurate source for monitoring SPO 2 and as one of the inaccurate sources for monitoring heart rate and respiratory rate. 9 The two blood pressure devices were manually operated and were not continuous.…”

“…The manual monitoring of a patient who is admitted and is lying in a hospital bed, done by healthcare professionals, faces much fewer problems than automated monitoring of a patient who is at home carrying out daily tasks [8]. One of the widely acknowledged and intrinsic challenges for wearable devices is the movement artifact [9]. Moreover, incorrectly attached or detached sensors, broken sensors, and noise can affect the calculation of the EWS that could lead to a false or -even worse -a missing alarm with all its consequences [10].…”

Heart Rate Monitoring using Sparse Spectral Curve Tracing

“…A tremendous research interest continues to contributing other noteworthy algorithms including Wiener Filtering and Phase Vocoder (WFPV) [7] and multiple initialization spectral peak tracking (MISPT) [8]. A detailed literature review can be found in [9].…”