Review on Heart-Rate Estimation from Photoplethysmography and Accelerometer Signals During Physical Exercise

Periyasamy, Vijitha; Pramanik, Manojit; Ghosh, Prasanta Kumar

doi:10.1007/s41745-017-0037-1

Cited by 11 publications

(10 citation statements)

References 59 publications

(68 reference statements)

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“…Therefore in this work it is decided to use an online dataset representative of the various case studies. The dataset used in this study [21] is the most used from literature for testing and developing HR estimation algorithms from PPG signals in presence of motion artifacts [11]. The data recorded in the dataset contains two-channel PPG signals and acceleration signals, other than a one-channel ECG signal, recorded simultaneously from subjects, all sampled at 125 Hz.…”

Section: Methodsmentioning

confidence: 99%

A Real-Time Algorithm for PPG Signal Processing During Intense Physical Activity

Gentili

Belli

Palma

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Photopletismography (PPG) is a simple, low cost and noninvasive technique, implemented by pulse-oximeters to measures several clinical parameters, such as hearth rate, oxygen saturation (Spo2), respiration and other clinical diseases. Although monitoring of these parameters at rest does not present particular problems, processing PPG signals during intensive physical activity is still a challenge, due to the presence of motion artifacts that affect its true estimation. In our work, a novel time-frequency based algorithm is presented to properly reconstruct PPG signal during intensive physical activity with respect to the ECG signal reference. Starting from raw PPG and acceleration signals, the proposed algorithm initially removes motion artifacts, providing an accurate heart rate estimation. Subsequently, it reconstructs PPG waveform based on both the heart rate information previously computed and the optimal selection of frequency-domain components representing PPG signal. Evaluating our proposed method on a dataset containing signals acquired during high speed running, we found for heart rate estimation an average absolute error of 1.20 BPM and very similar frequency dynamics between the ECG reference and PPG reconstructed HRV time series from a physiological point of view based on visual inspection.

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Section: Methodsmentioning

confidence: 99%

A Real-Time Algorithm for PPG Signal Processing During Intense Physical Activity

Gentili

Belli

Palma

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…In ambulatory settings, an additional source of error in IBI estimation arises from motion artifacts, which especially affect PPG measurements. Over the past years, artifact identification and removal has been an active field of research on cardiac signals from both ECG and PPG [16,17]. A more comprehensive approach consists in computing a signal quality index (SQI) on the cardiac recordings.…”

Section: Wearable Technology For Heart Rate Estimationmentioning

confidence: 99%

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Vila¹,

Godin²,

Charbonnier³

et al. 2021

Preprint

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Wearable cardiac sensors pave the way to advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to mo-tion artifacts that can be timely removed from the recordings. This leads to frequent data loss in the HR signal, especially for commercial devices based on photoplethysmography (PPG). The cur-rent study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from com-mercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an out-lier rejection process, our quality index was used to isolate portions of ECG-based HR signal that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy for estimating the mean HR, poor accuracy for short-term HRV features and moderate accuracy for longer-term HRV features. Levels of error could be substantially reduced by using our quality index to identify time windows with few or no missing data.

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“…While various articles rigorously review artifact reduction schemes for EEG (Xu et al, 2017;Jiang et al, 2019;Shad et al, 2020) and PPG (Periyasamy et al, 2017;Biswas et al, 2019), this article covers cross-border EEG motion artifact removal schemes that can be utilized at each part of the entire acquisition system: from the recording front-end hardware to the processing algorithm software, emphasizing the necessity of a diversified approach on the artifact issues. Furthermore, in-ear EEG researches that are actively studied in recent years are carefully reviewed from a perspective of the motion artifact removal.…”

Section: Introductionmentioning

confidence: 99%

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

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Review on Heart-Rate Estimation from Photoplethysmography and Accelerometer Signals During Physical Exercise

Cited by 11 publications

References 59 publications

A Real-Time Algorithm for PPG Signal Processing During Intense Physical Activity

A Real-Time Algorithm for PPG Signal Processing During Intense Physical Activity

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

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