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. Because of 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. This paper explores three important
factors 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 while 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.