Key result
Automatic artefact identification and filtering algorithms for ECG data in mHealth systems can be evaluated and compared using signal quality, interpretation quality, and computational complexity.
This paper provides a framework for evaluating ECG artefact identification algorithms in resource-constrained mHealth systems based on signal quality, interpretation quality, and computational complexity.
Aids ECG artefact algorithm selection in wireless mHealth sensors; leaves open prospective clinical validation.
Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Owing to the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. Three important factors are explored that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm, whereas the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.
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Imtiaz et al. (2016) studied ECG artefact identification. Automatic artefact identification and filtering algorithms was evaluated on Algorithm evaluation metrics (signal quality, interpretation quality, computational complexity). Automatic artefact identification and filtering algorithms for ECG data in mHealth systems can be evaluated and compared using signal quality, interpretation quality, and computational complexity.