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Sensors 2014, 14(12), 22532-22551; doi:10.3390/s141222532

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Embedded Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1007 Lausanne, Switzerland
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Received: 14 October 2014 / Revised: 12 November 2014 / Accepted: 19 November 2014 / Published: 27 November 2014
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
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Abstract

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject’s bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections. View Full-Text
Keywords: embedded signal processing; wireless body sensor nodes; electrocardiogram; classification embedded signal processing; wireless body sensor nodes; electrocardiogram; classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Braojos, R.; Beretta, I.; Ansaloni, G.; Atienza, D. Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes. Sensors 2014, 14, 22532-22551.

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