CorNET: Deep Learning framework for PPG based Heart Rate Estimation and Biometric Identification in Ambulant Environment

Biswas, Dwaipayan and Everson, Luke and Liu, Muqing and Panwar, Madhuri and Verhoef, Bram and Patrika, Shrishail and Kim, Chris H and Acharyya, Amit and Van Hoof, Chris and Konijnenburg, Mario and Van Helleputte, Nick (2019) CorNET: Deep Learning framework for PPG based Heart Rate Estimation and Biometric Identification in Ambulant Environment. IEEE Transactions on Biomedical Circuits and Systems. ISSN 1932-4545

Full text not available from this repository. (Request a copy)

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 (ECG), 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 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 modelling 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: a) regression layer - having a single neuron to predict HR; b) classification layer - two neurons which identifies 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 BPM 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.

[error in script]

IITH Creators: