DOI: 10.14457/tu.the.2014.252
title: A study on continuous cardiac monitoring through body sensor networks

description: With current advances in wireless and sensor technologies, Body Sensor Networks (BSNs) have been adopted as promising tools that provide a platform for pervasive healthcare monitoring. These technologies lead to a revolution in traditional diagnosis tools and methods for healthcare systems. In continuous cardiac monitoring using wireless BSNs, a quality of electrocardiogram (ECG) signals can be frequently deteriorated due to several factors, e.g., noises arising from body movements occurring from activities of daily living (ADLs), low battery power, and network transmission problems, often resulting in high false alarm rates. To solve these problems, a study on an efficient and effective approach for developing a wireless continuous cardiac monitoring system is essentially required. In this dissertation, we present the development of continuous cardiac monitoring using wireless BSNs. First, we present a technique for automatic arrhythmia classification that is applicable to ECG signals acquired using wireless recording systems. Using features relating to heartbeat intervals and ECG morphologies, a machine-learning-based classification model is used for classifying heartbeats into the five arrhythmia classes recommended by the Association for the Advancement of Medical Instrumentation (AAMI) standard, i.e., non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. Secondly, we present a framework for BSN-based cardiac monitoring using an ECG sensor and a 3D accelerometer, where classification models constructed from machine learning algorithms are used for labeling ECG signals with arrhythmia types and signal quality levels, and labeling acceleration signals with ADL types. A rulebased expert system is used for combining the classification results in order to make a final decision. Finally, we present a knowledge-based framework for predicting types of arrhythmia from ECG signals acquired using wireless BSNs. Using terms in an ECG signal ontology, ECG signals are annotated by locating the positions of elementary waves, including their onset, offset, and peak positions. Rules are used for extracting features, e.g., heart rates, PR intervals, and QRS intervals, from annotated signals. An arrhythmia indicator ontology is constructed in order to define concepts representing different characteristics of ECG waveforms, which are then used for defining necessary and sufficient conditions for arrhythmia classification of signal portions.For framework evaluation, a publicly-available standard MIT-BIH arrhythmia dataset and three datasets acquired using wireless BSNs are employed. The BSN datasets include ECG signals recorded from a patient simulator, and ECG signals and 3D acceleration signals captured from young subjects and elderly subjects, while they are performing ADLs. All the datasets are used for evaluating arrhythmia classification. All but the BSN dataset generated from a patient simulator are employed for evaluating signal quality classification. The BSN datasets captured from young subjects and elderly subjects are used for evaluating activity classification and false alarm reduction. The experimental results demonstrate that the proposed frameworks can be used for enhancing the performance of continuous cardiac monitoring using wireless sensors, by taking signal quality levels and ADL types into considerations.