Localization of Noise Sources in a Multilead Electrophysiological Record

Electrophysiological recordings from brain, heart, stomach or muscles usually contain noise, which decreases the clarity of desired signal. The term ‘noise’ is commonly applied to a variety of extrinsic factors: spontaneous muscle activation, skin response, interferences or limitations in electrical circuits. Usually the noise is identified by its temporal or spectral characteristics provided by statistical models. This paper proposes a two-compartment model of noise allowing for rough localization of its source with a search of coincidence in a multilead record. Accordingly to characteristics of noise sources expected in each of these compartment, the algorithm performs correlation, coherence and principal component analysis to distinguish equipment-related noise from a possible extra physiological activity taking place within the body. Physiological activities can be then localized with use of the independent component analysis with regard to the electrode position and, with applying of extra knowledge, classified as noise or signal. The proposed algorithm was tested with synthetic and original ECG and shows 43-95% of detection efficiency, depending on the source and amplitude of noise. It can be beneficial for assessment of physiological record quality, studying coincident physiological processes and research on noise characteristics.

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