ECG artefact identification and removal in mHealth systems for continuous patient monitoring. Issue 3 (15th September 2016)

• Record Type: Journal Article
• Authors: Imtiaz, Syed Anas; Mardell, James; Saremi–Yarahmadi, Siavash; Rodriguez–Villegas, Esther
• Is Part Of: Healthcare technology letters. Volume 3:Issue 3(2016)
• Page Start: 171
• Page End: 176
• Publication Date: 2016-09-15
• Subjects: electrocardiography -- patient monitoring -- medical signal processing -- telemedicine -- data acquisition -- biomechanics -- biomedical equipment -- computational complexity -- filtering theory -- wireless sensor networks; ECG artefact identification -- ECG artefact removal -- mHealth systems -- continuous patient monitoring systems -- automatic processing -- intelligent algorithms -- data acquired sections -- patient movement -- sensor placement -- sensor interference -- medical diagnosis -- automatic artefact identification algorithms -- signal quality -- interpretation quality -- computational complexity -- filtering algorithms -- electrocardiography data -- wireless sensor network; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2016.0020
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : 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.