Real-Time HR Estimation from wrist PPG using Binary LSTMs

Leandro Giacomini Rocha; Nick Van Helleputte; Muqing Liu; Dwaipayan Biswas; Bram Ernst Verhoef; Sergio Bampi; Chris H. Kim; Chris Van Hoof; Mario Konijnenburg; Marian Verhelst

doi:10.1109/BIOCAS.2019.8918726

Real-Time HR Estimation from wrist PPG using Binary LSTMs

Leandro Giacomini Rocha
, Nick Van Helleputte
, Muqing Liu
, Dwaipayan Biswas
, Bram Ernst Verhoef
, Sergio Bampi
, Chris H. Kim
, Chris Van Hoof
, Mario Konijnenburg
, Marian Verhelst

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Wrist-worn photoplethysmography (PPG) sensors present a popular alternative to electrocardiogram recording for heart rate (HR) estimation. However, their accuracy is limited by motion artifacts inherent in ambulatory settings. In this paper, we propose a binarized neural network framework, b-CorNET, to efficiently estimate HR from single-channel wrist PPG signals during intense physical activity. The model comprises two binary convolution neural network layers followed by two binary long short-Term memory (b-LSTM) layers and a dense layer working on quantized PPG data. The proposed framework achieves an MAE of 3.75±3.05 bpm when evaluated on 12 IEEE SPC subjects. Furthermore, a novel, low-complexity architecture for the b-LSTM layers is proposed and efficiently mapped on a Xilinx Virtex5 FPGA, enabling HR computation.

Original language	English (US)
Title of host publication	BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509006175
DOIs	https://doi.org/10.1109/BIOCAS.2019.8918726
State	Published - Oct 2019
Event	2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, Japan Duration: Oct 17 2019 → Oct 19 2019

Publication series

Name	BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference	2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019
Country/Territory	Japan
City	Nara
Period	10/17/19 → 10/19/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Binary LSTM
CNN
FPGA
PPG

Publisher link

10.1109/BIOCAS.2019.8918726

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Rocha, L. G., Helleputte, N. V., Liu, M., Biswas, D., Verhoef, B. E., Bampi, S., Kim, C. H., Van Hoof, C., Konijnenburg, M., & Verhelst, M. (2019). Real-Time HR Estimation from wrist PPG using Binary LSTMs. In BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings Article 8918726 (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2019.8918726

Real-Time HR Estimation from wrist PPG using Binary LSTMs. / Rocha, Leandro Giacomini; Helleputte, Nick Van; Liu, Muqing et al.
BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8918726 (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rocha, LG, Helleputte, NV, Liu, M, Biswas, D, Verhoef, BE, Bampi, S, Kim, CH, Van Hoof, C, Konijnenburg, M & Verhelst, M 2019, Real-Time HR Estimation from wrist PPG using Binary LSTMs. in BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings., 8918726, BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019, Nara, Japan, 10/17/19. https://doi.org/10.1109/BIOCAS.2019.8918726

Rocha LG, Helleputte NV, Liu M, Biswas D, Verhoef BE, Bampi S et al. Real-Time HR Estimation from wrist PPG using Binary LSTMs. In BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8918726. (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings). doi: 10.1109/BIOCAS.2019.8918726

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title = "Real-Time HR Estimation from wrist PPG using Binary LSTMs",

abstract = "Wrist-worn photoplethysmography (PPG) sensors present a popular alternative to electrocardiogram recording for heart rate (HR) estimation. However, their accuracy is limited by motion artifacts inherent in ambulatory settings. In this paper, we propose a binarized neural network framework, b-CorNET, to efficiently estimate HR from single-channel wrist PPG signals during intense physical activity. The model comprises two binary convolution neural network layers followed by two binary long short-Term memory (b-LSTM) layers and a dense layer working on quantized PPG data. The proposed framework achieves an MAE of 3.75±3.05 bpm when evaluated on 12 IEEE SPC subjects. Furthermore, a novel, low-complexity architecture for the b-LSTM layers is proposed and efficiently mapped on a Xilinx Virtex5 FPGA, enabling HR computation.",

keywords = "Binary LSTM, CNN, FPGA, PPG",

author = "Rocha, \{Leandro Giacomini\} and Helleputte, \{Nick Van\} and Muqing Liu and Dwaipayan Biswas and Verhoef, \{Bram Ernst\} and Sergio Bampi and Kim, \{Chris H.\} and \{Van Hoof\}, Chris and Mario Konijnenburg and Marian Verhelst",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 ; Conference date: 17-10-2019 Through 19-10-2019",

year = "2019",

month = oct,

doi = "10.1109/BIOCAS.2019.8918726",

language = "English (US)",

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AU - Rocha, Leandro Giacomini

AU - Helleputte, Nick Van

AU - Liu, Muqing

AU - Biswas, Dwaipayan

AU - Verhoef, Bram Ernst

AU - Bampi, Sergio

AU - Kim, Chris H.

AU - Van Hoof, Chris

AU - Konijnenburg, Mario

AU - Verhelst, Marian

PY - 2019/10

Y1 - 2019/10

N2 - Wrist-worn photoplethysmography (PPG) sensors present a popular alternative to electrocardiogram recording for heart rate (HR) estimation. However, their accuracy is limited by motion artifacts inherent in ambulatory settings. In this paper, we propose a binarized neural network framework, b-CorNET, to efficiently estimate HR from single-channel wrist PPG signals during intense physical activity. The model comprises two binary convolution neural network layers followed by two binary long short-Term memory (b-LSTM) layers and a dense layer working on quantized PPG data. The proposed framework achieves an MAE of 3.75±3.05 bpm when evaluated on 12 IEEE SPC subjects. Furthermore, a novel, low-complexity architecture for the b-LSTM layers is proposed and efficiently mapped on a Xilinx Virtex5 FPGA, enabling HR computation.

AB - Wrist-worn photoplethysmography (PPG) sensors present a popular alternative to electrocardiogram recording for heart rate (HR) estimation. However, their accuracy is limited by motion artifacts inherent in ambulatory settings. In this paper, we propose a binarized neural network framework, b-CorNET, to efficiently estimate HR from single-channel wrist PPG signals during intense physical activity. The model comprises two binary convolution neural network layers followed by two binary long short-Term memory (b-LSTM) layers and a dense layer working on quantized PPG data. The proposed framework achieves an MAE of 3.75±3.05 bpm when evaluated on 12 IEEE SPC subjects. Furthermore, a novel, low-complexity architecture for the b-LSTM layers is proposed and efficiently mapped on a Xilinx Virtex5 FPGA, enabling HR computation.

KW - Binary LSTM

KW - CNN

KW - FPGA

KW - PPG

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DO - 10.1109/BIOCAS.2019.8918726

M3 - Conference contribution

AN - SCOPUS:85077044436

T3 - BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

BT - BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019

Y2 - 17 October 2019 through 19 October 2019

ER -

Real-Time HR Estimation from wrist PPG using Binary LSTMs

Abstract

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Bibliographical note

Keywords

Publisher link

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