Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds.

Background: Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised.

Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel sounds.

Bowel sound (BS) signals can be used clinically as useful indicators of bowel motility. In this study, we devised a modified iterative kurtosis-based detector algorithm, in order to enhance the de-noising performance of BS signals, and an estimation algorithm of bowel motility based on the regression modeling of the jitter and shimmer of BS signals obtained by auscultation. The correlation coefficient, coefficient of determination and errors between the colon transit times measured by a conventional radiograph and the corresponding values estimated by our method were 0.

Availability of a newly devised ambulatory urodynamics monitoring system based on personal device assistance in patients with spinal cord injury.

Conventional urodynamics systems have been widely used for the assessment of bladder functions. However, they have some drawbacks due to the unfamiliar circumstances for the patient, restrictive position during the test, expense and immovability of the instrument as well as the unphysiological filling of the bladder. To mitigate these problems, we developed a fully ambulatory urodynamics monitoring system, which enables the abdominal pressure to be measured in a non-invasive manner, as well as the manual recording of various events such as the bladder sensations or leakage of urine.

Portable measurement system for the objective evaluation of the spasticity of hemiplegic patients based on the tonic stretch reflex threshold.

The clinical scales used for the evaluation of the spasticity have some drawbacks, in spite of their simplicity and ease of assessment, and their inter- and intra-rater reliability remains controversial. The aim of this study is to develop a portable system for the objective and reliable evaluation of the spasticity based on the K-means clustering of the tonic stretch reflex threshold (TSRT) and to compare the discrimination performance of the level of spasticity determined by our method with that by the conventional modified Ashworth scale (MAS). Fifteen hemiplegic patients (7 males and 8 females, age: 63.

An acoustical evaluation of knee sound for non-invasive screening and early detection of articular pathology.

Knee sound signals generated by knee movement are sometimes associated with degeneration of the knee joint surface and such sounds may be a useful index for early disease. In this study, we detected the acoustical parameters, such as the fundamental frequency (F0), mean amplitude of the pitches, and jitter and shimmer of knee sounds, and compared them according to the pathological conditions. Six normal subjects (4 males and 2 females, age: 28.

Implementation of a multi-functional ambulatory urodynamics monitoring system based on newly devised abdominal pressure measurement.

The measurement of the rectal pressure is considered to be the 'gold standard' for the assessment of the abdominal pressure. However, conventional rectal catheters can cause erroneous results and are not comfortable for the patients. To reduce these problems, we devised a non-invasive technique for the measurement of the abdominal pressure using the parametric curve fitting method, based on linear, polynomial, exponential or sine equation modeling, between the rectal pressure and electromyographic (sEMG) signals recorded simultaneously from the abdomen.

An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis.

Vibroarthrographic (VAG) signals, generated by human knee movement, are non-stationary and multi-component in nature and their time-frequency distribution (TFD) provides a powerful means to analyze such signals. The objective of this paper is to improve the classification accuracy of the features, obtained from the TFD of normal and abnormal VAG signals, using segmentation by the dynamic time warping (DTW) and denoising algorithm by the singular value decomposition (SVD). VAG and knee angle signals, recorded simultaneously during one flexion and one extension of the knee, were segmented and normalized at 0.