2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) 2019
DOI: 10.1109/biocas.2019.8918711
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Deep Recurrent Neural Network for Extracting Pulse Rate Variability from Photoplethysmography During Strenuous Physical Exercise

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Cited by 18 publications
(4 citation statements)
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“…Heart Rate Variability (HRV) is the continuous fluctuation of period length between cardiac cycles, which can be used for the diagnosis of cardiovascular diseases, such as myocardial infarction and cardiac arrhythmia. In Reference [45], an RNN based on bidirectional long short-term memory (biLSTM) is introduced for accurate PPG cardiac period segmentation to derive three important indexes for HRV estimation.…”
Section: Related Workmentioning
confidence: 99%
“…Heart Rate Variability (HRV) is the continuous fluctuation of period length between cardiac cycles, which can be used for the diagnosis of cardiovascular diseases, such as myocardial infarction and cardiac arrhythmia. In Reference [45], an RNN based on bidirectional long short-term memory (biLSTM) is introduced for accurate PPG cardiac period segmentation to derive three important indexes for HRV estimation.…”
Section: Related Workmentioning
confidence: 99%
“…To analysis, the effect of MA on acquired bio-signal using the wearable device while performing physical activities; a comparison is performed between noisy signal (MA corrupted) and clean signal. A data set (Xu et al, 2019(Xu et al, , 2020 containing noisy PPG, clean PPG signal and motion data were used to evaluate the effect of MA.…”
Section: Analysis Of the Effect Of Motion Artifactsmentioning
confidence: 99%
“…For this reason, in many studies, the PPG signal was recorded during experiments inducing artifacts. Most often, they included: (1) baseline measurements [19][20][21][22][23]; (2) controlled movements of an arm or fingers (please note that the experiments with the arm and finger movements are compatible because the choice of the type depended on the position of the PPG sensor on a wrist or finger) [20,[24][25][26][27][28][29][30][31][32][33], (3) breathing with different patterns [22,34], (4) walking or running on a treadmill with a regulated pace [21,24,25,35,36], or (5) tapping the sensor [26]. In many studies, the correctness of PPG processing was assessed by comparing it with the results of ECG processing, which was taken as the reference data.…”
Section: Introductionmentioning
confidence: 99%