Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

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

A MULTI-LEAD ECG CLASSIFICATION BASED ON RANDOM PROJECTION FEATURES

This paper presents a novel method for classification of multilead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro-fuzzy classifier. Such a model can be easily implemented on portable systems for practical applications in both health monitoring and diagnostic purposes. Moreover, the RP implementation on portable systems is very challenging featuring both energy efficiency and feasibility. The proposed method is tested on a 12-lead ECG database consisting of 20 beats during normal sinus rhythm, 20 beats with myocardial infarction and 20 beats showing cardiomyopathy for 60 different subjects. The experiments give a recognition rate of 100 % for a small number of RP coefficients (only 25), i.e. after a considerable dimensionality reduction of the input ECG signal. The results are very promising, not only from the classification performance point of view, but also while targeting a low-complexity feature extraction in terms of computation requirements and memory usage for real-time operation on a wireless wearable sensor platform. Index Terms — random projections, adaptive neuro-fuzzy classification, automatic multi-lead ECG, ECG dimensionality reduction, 1