Online ECG quality assessment for context-aware wireless body area networks

Electrocardiogram (ECG) signals are commonly used in wireless body area networks (WBAN), particularly for patient monitoring applications. ECGs, however, are sensitive to various types of noise sources, including but not limited to: powerline interference, movement, muscle and breathing artefacts. Such sensitivity is increased when burgeoning lower-cost sensors, such as textile ECG sensors, are used. Transmission of noisy ECGs can be troublesome for various reasons. For example, it consumes bandwidth, battery life, and storage space with signals that convey little cardiac information. Moreover, noisy signals may cause false alarms in automated patient monitoring systems, thus increasing the burden on medical personnel. In this paper, we describe a new ECG quality index based on the so-called modulation spectral signal representation. Two classifiers are tested to discriminate between usable and non-usable ECG segments. When applied within a quality-aware WBAN application, we show savings of up to 65% in storage space relative to a traditional scheme.