Evaluation of HRV estimation algorithms from PPG data using neural networks

Heart rate variability (HRV) is a powerful measure to gain information on the activation of the central nervous system and is thus a strong indicator for the overall health and emotional state of a person. Currently, the gold standard for HRV analysis is the examination of R-peaks in electrocardiogr...

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Autores principales:	Wittenberg Th., Koch R., Pfeiffer N., Lang N., Struck M., Amft O., Eskofier B.
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	photoplethysmography ppg heart-rate variability hrv neural networks Medicine R
Acceso en línea:	https://doaj.org/article/f7b0e9de10784e8798fb417cafe37054
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spelling	oai:doaj.org-article:f7b0e9de10784e8798fb417cafe370542021-12-05T14:10:43ZEvaluation of HRV estimation algorithms from PPG data using neural networks2364-550410.1515/cdbme-2020-3130https://doaj.org/article/f7b0e9de10784e8798fb417cafe370542020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3130https://doaj.org/toc/2364-5504Heart rate variability (HRV) is a powerful measure to gain information on the activation of the central nervous system and is thus a strong indicator for the overall health and emotional state of a person. Currently, the gold standard for HRV analysis is the examination of R-peaks in electrocardiograms (ECG), which requires a placement of electrodes on the torso. This is often impracticable, especially for the use in daily routines or 24/7 measurements. Photoplethysmograms (PPG) are an alternative to ECG assessment and are easier to acquire, e.g. by using fitness trackers or smart watches. Nevertheless, PPG data is more susceptible to motion artifacts. Hence, goal of this work is to develop and evaluate an artificial neural network (ANN) approach to estimate the R-peak locations in complex PPG signals. Public data collections were used as benchmark to compare our ANN-based approach to state-of-the-art methods. Results show that ANNs can improve HRV estimation during motion. HRV estimations from baseline methods (decision-tree based and automatic multiscalebased peak detection) were compared with the best performing neural network (3L-GRU) using the TROIKA dataset with respect to reference parameters obtained from a manual selection of the peaks in ECG data. In most cases, the neural network based HRV estimation was closer to the reference HRV compared to baseline methods (lower μ and σ ) Also, σ is smaller for the best performing ANN approach across most HRV parameters. Inclusion of another PPG or acceleration channel did not affect HRV estimation. Although, the neural network learning approach outperforms conventional methods, the examined PPG-based HRV estimation has still accuracy limitations. Nonetheless, the proposed estimation approach opens up new directions for further improvement.Wittenberg Th.Koch R.Pfeiffer N.Lang N.Struck M.Amft O.Eskofier B.De Gruyterarticlephotoplethysmographyppgheart-rate variabilityhrvneural networksMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 505-509 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	photoplethysmography ppg heart-rate variability hrv neural networks Medicine R
spellingShingle	photoplethysmography ppg heart-rate variability hrv neural networks Medicine R Wittenberg Th. Koch R. Pfeiffer N. Lang N. Struck M. Amft O. Eskofier B. Evaluation of HRV estimation algorithms from PPG data using neural networks
description	Heart rate variability (HRV) is a powerful measure to gain information on the activation of the central nervous system and is thus a strong indicator for the overall health and emotional state of a person. Currently, the gold standard for HRV analysis is the examination of R-peaks in electrocardiograms (ECG), which requires a placement of electrodes on the torso. This is often impracticable, especially for the use in daily routines or 24/7 measurements. Photoplethysmograms (PPG) are an alternative to ECG assessment and are easier to acquire, e.g. by using fitness trackers or smart watches. Nevertheless, PPG data is more susceptible to motion artifacts. Hence, goal of this work is to develop and evaluate an artificial neural network (ANN) approach to estimate the R-peak locations in complex PPG signals. Public data collections were used as benchmark to compare our ANN-based approach to state-of-the-art methods. Results show that ANNs can improve HRV estimation during motion. HRV estimations from baseline methods (decision-tree based and automatic multiscalebased peak detection) were compared with the best performing neural network (3L-GRU) using the TROIKA dataset with respect to reference parameters obtained from a manual selection of the peaks in ECG data. In most cases, the neural network based HRV estimation was closer to the reference HRV compared to baseline methods (lower μ and σ ) Also, σ is smaller for the best performing ANN approach across most HRV parameters. Inclusion of another PPG or acceleration channel did not affect HRV estimation. Although, the neural network learning approach outperforms conventional methods, the examined PPG-based HRV estimation has still accuracy limitations. Nonetheless, the proposed estimation approach opens up new directions for further improvement.
format	article
author	Wittenberg Th. Koch R. Pfeiffer N. Lang N. Struck M. Amft O. Eskofier B.
author_facet	Wittenberg Th. Koch R. Pfeiffer N. Lang N. Struck M. Amft O. Eskofier B.
author_sort	Wittenberg Th.
title	Evaluation of HRV estimation algorithms from PPG data using neural networks
title_short	Evaluation of HRV estimation algorithms from PPG data using neural networks
title_full	Evaluation of HRV estimation algorithms from PPG data using neural networks
title_fullStr	Evaluation of HRV estimation algorithms from PPG data using neural networks
title_full_unstemmed	Evaluation of HRV estimation algorithms from PPG data using neural networks
title_sort	evaluation of hrv estimation algorithms from ppg data using neural networks
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/f7b0e9de10784e8798fb417cafe37054
work_keys_str_mv	AT wittenbergth evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks AT kochr evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks AT pfeiffern evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks AT langn evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks AT struckm evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks AT amfto evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks AT eskofierb evaluationofhrvestimationalgorithmsfromppgdatausingneuralnetworks
_version_	1718371816885452800

Evaluation of HRV estimation algorithms from PPG data using neural networks

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