A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

• Published in: SLAS technology 2018-06, Vol.23 (3), p.269-280
• Main Authors: Sengottuvel, S., Khan, Pathan Fayaz, Mariyappa, N., Patel, Rajesh, Saipriya, S., Gireesan, K.
• Format: Article
• Language: English
• Subjects: Algorithms; Computational Biology - methods; Computer Simulation; Humans; Models, Theoretical; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Stomach - diagnostic imaging; Stomach Diseases - diagnosis
• ISSN: 2472-6303; 2472-6311
• DOI: 10.1177/2472630318756903

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.