title: Early classification of pathological heartbeats on wireless body sensor nodes.
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.
authors.affiliation: Embedded Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1007 Lausanne, Switzerland. ruben.braojoslopez@epfl.ch.
authors.forename: Rubén
authors.initials: R
authors.lastname: Braojos
authors.affiliation: Embedded Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1007 Lausanne, Switzerland. ivan.beretta@epfl.ch.
authors.forename: Ivan
authors.initials: I
authors.lastname: Beretta
authors.affiliation: Embedded Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1007 Lausanne, Switzerland. giovanni.ansaloni@epfl.c.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Ansaloni
authors.affiliation: Embedded Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1007 Lausanne, Switzerland. david.atienza@epfl.ch.
authors.forename: David
authors.initials: D
authors.lastname: Atienza
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D003195:Computer Communication Networks; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D042241:Early Diagnosis; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D017143:Fuzzy Logic; D006339:Heart Rate; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059015:Wireless Technology