Zhishun Wang

Cisapride provides symptomatic relief in functional dyspepsia associated with gastric myoelectrical abnormality

We evaluated the effects of cisapride (10 mg t.d.s. and 20 mg b.d.) on gastrointestinal symptoms and gastric myoelectrical activity in patients with functional dyspepsia. Myoelectrical activity was measured by electrogastrography.

Is there a one-to-one correlation between gastric emptying of liquids and gastric myoelectrical or motor activity in dogs?

The aim of this study was to investigate the correlation of gastric emptying with gastric myoelectrical activity recorded from internal serosal electrodes and with gastric motility measured from strain gauges. The study was performed in eight healthy dogs chronically implanted with four pairs of electrodes and two strain gauges on the gastric serosa and equipped with a duodenal fistula for the assessment of gastric emptying. Each dog was fed four liquid test meals on four separate days with identical calories (320 kcal) and volume (473 ml). A correlation was found between gastric emptying and the energy of contraction in the frequency band of 3.75–7.50 cycles/min during the first 30 min after the meal (r = 0.46, P < 0.05). While none of the parameters of gastric myoelectrical activity was correlated with gastric emptying, two major parameters, percentage of regular gastric slow waves and percentage of slow-wave coupling, were found to be associated with delayed gastric emptying. A significant correlation was found between the frequency of gastric slow waves and that of the contractions in both fasting (r = 0.83, P < 0.001) and fed state (r = 0.70, P < 0.001 at 30 min, r = 0.86, P < 0.001 at 60 min). It was concluded that gastric emptying is correlated with the strength of gastric contractions in a frequency range identical to that of the gastric slow waves, and there is no one-to-one correlation between gastric emptying of liquids and any parameters of gastric myoelectrical activity. However, major parameters of gastric myoelectrical activity are associated with gastric emptying.

A generalized fast algorithm for n-D discrete cosine transform and its application to motion picture coding

In this paper, a generalized fast computational algorithm for the n-dimensional discrete cosine transform (DCT) of length N=2/sup m/ (m/spl ges/2) is presented. The developed algorithm is proved and its efficiency is evaluated theoretically. The theoretical results show that compared with the conventional method of computing the one-dimensional along n directions, the number of multiplications needed by our algorithm is only 1/n of that required by the conventional method; for the total number of additions, the latter is a bit more when N/spl les/8 and much fewer when N/spl ges/16 than the former. To validate the proposed algorithm, we take the case when n=3 as an example and apply it to motion-picture coding. The results show that our method is superior to MPEG-2 in speed and coding performance. The algorithm is clearly described and it is easy to make a computer program for implementation.

Blind separation of slow waves and spikes from gastrointestinal myoelectrical recordings

Myoelectrical recordings of the gut contain slow waves (slow rhythmicity) and spikes (fast rhythmicity). While the slow wave determines the frequency and propagation of gastrointestinal contractions, spike activities are directly associated with the contractions. Traditionally, spikes are extracted from the myoelectrical recording using high-pass/bandpass filters. Due to the sharp rising edge (high-frequency component) of the slow wave, the conventional method is not accurate in the separation of the slow wave and spikes, although it has been used for years. In this paper, a novel and fast blind source separation algorithm is developed. Experimental results show that the proposed method was able to accurately extract spike activities from the myoelectrical recordings obtained in dogs and that its performance was not affected by the high-frequency components of the slow wave.

Detection of gastric slow wave uncoupling from multi-channel electrogastrogram: validations and applications

Current methodology of single channel electrogastrography is unable to detect coupling or uncoupling of gastric slow waves, which is crucial for gastric emptying. In this study, a new methodology, called cross‐spectral analysis method, was established to compute the coupling percentage of multi‐channel gastric slow waves recorded using serosal electrodes and electrogastrogram (EGG). Two experiments were performed to validate the method and demonstrate its applications in clinical research. In experiment 1, simultaneous recordings of gastric slow waves were made in five dogs from serosal electrodes and cutaneous electrodes. In experiment 2, four‐channel fasting EGGs were made in 10 volunteers for 30 min during waking and 30 min during non‐rapid eye movement (REM) sleep. The validation study (experiment 1) showed that the slow wave coupling calculated from the EGGs was correlated with that computed from the serosal recordings. The gastric slow wave coupling percentages detected from both serosal and cutaneous recordings were significantly impaired during vasopressin infusion (6.3 ± 2.6 vs 62.4 ± 6.3, P < 0.001 for serosal recordings; 6.7 ± 3.0 vs 57.2 ± 2.7, P < 0.001 for cutaneous recordings), and the coupling percentages respectively calculated from serosal and cutaneous recordings were significantly correlated during the baseline recording period (R = 0.922, P < 0.05) and vasopressin infusion period (R = 0.916, P < 0.05). In experiment 2, the gastric slow wave became less coupled when healthy volunteers fell asleep. The percentage of slow wave coupling calculated from the EGGs was 68.2 ± 17.9% during waking but 41.9 ± 20.8 during non‐REM sleep (P < 0.05). The method developed in this study is reliable for the detection of slow wave uncoupling from multi‐channel EGGs. Gastric slow wave coupling is impaired during vasopressin infusion and sleep. These data suggest that this method has potential applications in physiological and clinical studies.

Time-frequency analysis of heart rate variability using short-time Fourier analysis

This study was done to introduce new parameters derived by time frequency analysis of heart rate variability data. Four simulation experiments were carried out to compare the short-time Fourier transform (STFT) analysis method to the traditional overall spectral analysis method. Sinusoidal signals were generated with identical total power in the high-frequency band, but varying time-frequency and time-amplitude information. The STFT method was also applied to heart rate variability data from the stages of normal human sleep. Data analysis included computation of the power in the high-frequency band by overall spectral analysis. The instability coefficients (ICs) of the frequency and power in the high-frequency band were derived by STFT analysis. The ICs derived by the STFT method were able to describe time-frequency and time-amplitude variations in sinusoidal signals which contained identical total power in a specified frequency range. The ICs of the frequency and power were able to differentiate variations in vagal activity between the stages of human sleep and waking. The ICs represent new parameters derived by the STFT method, and allow the detection and quantification of short-lasting time-frequency and time-amplitude variations that remain obscured by overall spectral analysis.

Blind EGG separation using ICA neural networks

Blind electrogastrogram (EGG) signal separation technology by using neural network-based independent component analysis (ICA) is presented in this paper. The experimental results show that using this technology, the true EGG components can be separated from the multi-channel EGG data contaminated by measurement artefacts, such as respiratory, motion, electrocardiogram (ECG) and so on, even though no prior information on such contaminating can be obtained, which is closer to practical situations in clinical applications.

Cutaneous gastric electrical stimulation alters gastric motility in dogs: New option for gastric electrical stimulation?

Background and Aim:  We investigated the effects of cutaneous gastric electrical stimulation (CGES) on gastric myoelectrical activity, postprandial antral contractions and gastric tone in dogs.

A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals

In this paper the authors present a new neural network model, called the constrained smallest l/sub 1/-norm neural network (CSl/sub 1/ NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl/sub 1/ NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl/sub 1/ NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl/sub 1/ NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG.