Recursive running DCT algorithm and its application in adaptive filtering of surface electrical recording of small intestine

• Published in: Medical & biological engineering & computing Vol. 32; no. 3; pp. 317 - 322
• Main Authors: Lin, Z. Y., Chen, J. D. Z.
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer 01.05.1994
• Subjects: Algorithms; Biological and medical sciences; Digestive tract; Electrodiagnosis. Electric activity recording; Electrophysiology - methods; Gastrointestinal Motility - physiology; Humans; Intestine, Small - physiology; Investigative techniques, diagnostic techniques (general aspects); Mathematics; Medical sciences; Signal Processing, Computer-Assisted; Electric potential; Discrete transformation; Electrophysiology; Adaptive filter; Algorithm; Small intestine; Cosine transform; Surface; Electrodiagnosis; Signal processing; Electrogastrography; Electromyography; Recording; Technique; Signal analysis; Biomedical engineering
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/BF02512529

A rhythmic electrical signal is present in the human small intestine and can be recorded using surface electrodes. Surface electrical recordings of the small intestine contain severe interference that obscures the electrical signal of the small intestine. An adaptive system is proposed in the paper for the enhancement of the small-intestinal signal. To obtain better performance, adaptive signal enhancement is performed in the transform domain using a discrete cosine transform (DCT). A fast recursive algorithm is developed for the calculation of running DCT. The computational complexity of the proposed recursive algorithm is only 2/N (N = length of the adaptive filter) of the direct calculation of the running DCT. A series of simulations are conducted to investigate the performance of the proposed transform-domain adaptive filtering using DCT in comparison with time-domain adaptive filtering and with transform-domain adaptive filtering using a discrete Fourier transform (DFT). The parameters of the proposed adaptive system are optimised, and their effects on system performance are investigated. The application of the proposed method for the enhancement of the small-intestinal signal is presented and discussed.