CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment

Dwaipayan Biswas, Luke Everson, Muqing Liu, Madhuri Panwar, Bram Ernst Verhoef, Shrishail Patki, Chris H. Kim, Amit Acharyya, Chris Van Hoof, Mario Konijnenburg, Nick Van Helleputte

Research output: Contribution to journalArticlepeer-review

192 Scopus citations

Abstract

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-Term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram, suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only a wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-Term memory layers, followed by a dense output layer for modeling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: regression layer-having a single neuron to predict HR; classification layer-two neurons that identify a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47 ± 3.37 beats per minute for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

Original languageEnglish (US)
Article number8607019
Pages (from-to)282-291
Number of pages10
JournalIEEE transactions on biomedical circuits and systems
Volume13
Issue number2
DOIs
StatePublished - Apr 2019

Bibliographical note

Funding Information:
Manuscript received September 28, 2018; revised December 7, 2018 and January 4, 2019; accepted January 8, 2019. Date of publication January 10, 2019; date of current version March 22, 2019. This work was supported by the NSF IGERT Grant DGE-1069104. This paper was recommended by Associate Editor P. C. Chung. (Corresponding author: Dwaipayan Biswas.) D. Biswas, B.-E. Verhoef, C. Van Hoof, and N. Van Helleputte are with the imec, 3001 Leuven, Belgium (e-mail:,Dwaipayan.Biswas@imec.be; Bram. Verhoef@imec.be; Chris.VanHoof@imec.be; Nick.VanHelleputte@imec.be).

Publisher Copyright:
© 2007-2012 IEEE.

Keywords

  • Average heart rate
  • PPG
  • biometric
  • convolutional neural network
  • deep learning
  • long short-Term memory

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