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Aiming at the lack of signal preprocessing in current rectal signal research, a new 15th‐order Newton iterative fast independent component analysis (FastICA) algorithm is proposed to separate the signal. To solve the incomplete problem of the single channel rectal pressure signal, C‐C phase space reconstruction technology is used for multi‐dimensional signal reconstruction. In order to achieve the diagnosis of rectal dynamic after artificial anal sphincter implantation, the preprocessed rectal signal component is subjected to short‐time Fourier transforms for time–frequency analysis. This constitutes a complete rectal dynamic function diagnosis process. The non‐stationary simulation signal is constructed to verify the preprocessing method. A complete diagnostic experiment is performed on two rectal signals of different gut dynamic. Simulation results show that the proposed method can realise the preprocessing of non‐stationary signals. Through the rectal dynamic function experiment based FastICA‐STFT, two different rectal signals are distinguished, and the results are consistent with the clinical diagnosis, which has achieved the diagnosis of rectal motility. This study will provide support for the recovery of rectal dynamic function in patients with artificial anal sphincter implants. [ABSTRACT FROM AUTHOR]

Tissue classification using computer aided diagnosis can help automated decision making to aid clinical diagnosis. Classification of breast tissue based on spectral features of impedance loci has frequently been done to classify malignant tissue with further requirement of more complex classification methodologies needed to improve the characterisation. In current study, tissue classification is done using in vivo electrical impedance data of 18 human subjects, from four quadrants of breast, palm, nail, arm, bicep and classified using algorithms involving machine learning methodologies, specifically support vector machines (SVMs) that are supervised learning models. They consist of learning algorithms based on the principal of structural risk minimisation. Two methodologies of SVM have been used in this study: with data binning and data pruning and without data binning and data pruning. Data binning and data pruning have improved the sensitivity of the SVM from 76.76 to 89.23%, but the specificity has decreased from 76.23 to 74.15%. This is a pilot study towards testing the reliability of the developed electrical impedance measuring system and developing a data mining‐based decision making system into an electrical impedance spectroscopy system, to help users (physicians) with tissue classification leading to reliable objective decision making. [ABSTRACT FROM AUTHOR]