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Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout
Journal article   Open access   Peer reviewed

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

Anup Das, Paruthi Pradhapan, Willemijn Groenendaal, Prathyusha Adiraju, Raj Thilak Rajan, Francky Catthoor, Siebren Schaafsma, Jeffrey L. Krichmar, Nikil Dutt and Chris Van Hoof
Neural networks, v 99, pp 134-147
Mar 2018
DOI: https://doi.org/10.1016/j.neunet.2017.12.015
PMID: 29414535
url
http://arxiv.org/abs/1708.05356View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Electrocardiogram (ECG) Fuzzy c-Means clustering Homeostatic plasticity Liquid state machine Spike timing dependent plasticity (STDP) Spiking neural networks
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.

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53 citations in Web of Science
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Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Neurosciences
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