Assessment of slow wave propagation in multichannel electrogastrography by using noise-assisted multivariate empirical mode decomposition and cross-covariance analysis.

Electrogastrography (EGG) is a noninvasive technique for recording the myoelectrical activity of the stomach. An electrogastrographic signal recorded by using a four-channel system with electrodes placed on the surface of the skin is a mixture of a low-frequency gastric pacesetter potential known as a slow wave, electrical activity from other organs, and random noise. The aim of this work was to investigate the possibility of detecting the propagation of the gastric slow wave from multichannel EGG data. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) and cross-covariance analysis (CCA) are proposed as new detection tools. NA-MEMD was applied to attenuate the noise and extract the EGG signal from four channels, while CCA was performed to assess the time shift between the EGG signal channels. Validation of the method was performed using synthetic EGG signals and the methodology was tested on four young, healthy adults. After validation, the proposed method was applied for two kinds of human EGG data: 10-min (short) EGG data from the preprandial phase and 90-120-min (long) EGG data from the preprandial phase as well as the postprandial phase. The results obtained for both synthetic and human EGG data confirm that the proposed method could be a useful tool for assessing the propagation of slow waves. The time shift calculation from the preprandial phase of the EGG examination yielded more consistent results than the postprandial phase. The mean value of the slow wave time lag between neighbouring channels for synthetic data was found to be 4.99±0.47 s. In addition, it was confirmed that the proposed method, that is, NA-MEMD and CCA together, are robust to noise.

A new method for attenuation of respiration artifacts in electrogastrographic (EGG) signals.

Electrogastrography (EGG) is a test method designed for noninvasive assessment of gastric slow waves propagation. The EGG signal is obtained from the electrodes respectively arranged on the surface of the patient's abdomen. A significant problem during recording of the EGG signal is the elimination of disturbances occurring during registration and unwanted components of other signals such as: components of electrocardiographic (ECG), baseline drift or respiratory disturbances. These components are generally present in the signals registered from the surface of the abdomen of the patient. Since EGG frequency components partly overlap with the frequency components of respiratory artifacts, conventional band-pass digital or analog filtering may cause distortion in electrogastrographic signal. In the paper a method for removing respiratory interference occurring during registration of EGG signal and the effect of filtration on selected parameters of EGG signal analysis is presented. Respiratory artifacts are removed through the use of adaptive filter working in the DCT domain. The applied adaptive filtering method involves the use of the signal including respiratory disturbances. This signal is recorded synchronously with the EGG signal using a thermistor placed near the nose of the patient.

An application of both principal dynamic modes (PDM) and statistics for rehabilitation estimation related to the patients after ischemic brain stroke.

Ischemic Brain Stroke is considered to be one of the most significant reason causing mortality of patients. During first 60 days after brain stroke some critical symptoms are localized. Eventual registration of physiological parameters or biomedical signals e.g. heart rate variability within that time results in extremely difficult interpretation, most likely at the certain border of commonly understand sense. Therefore proper rehabilitation of such patients associated with well estimated results is very important as significantly decreases their mortality and leaves them in the highest possible well being. Due to rather bad quality of registered signals the need of more sophisticated processing method is necessary. The paper presents application of PDM for decomposition of HRV signals spectrum and separate estimation of behavior of both sympathetic and parasympathetic nervous activity. Simple ANOVA test applied for properly prepared data proved the anticipated results concerning significant difference of certain parameters e.g. sympatho-vagal balance estimated within mentioned 60 days period.

Hybrid feature vector extraction in unsupervised learning neural classifier.

Feature extraction and selection method as a preliminary stage of heart rate variability (HRV) signals unsupervised learning neural classifier is presented. Multi-domain, mixed new feature vector is created from time, frequency and time-frequency parameters of HRV analysis. The optimal feature set for given classification task was chosen as a result of feature ranking, obtained after computing the class separability measure for every independent feature. Such prepared a new signal representation in reduced feature space is the input to neural classifier based on introduced by Grosberg Adaptive Resonance Theory (ART2) structure. Test of proposed method carried out on the base of 62 patients with coronary artery disease divided into learning and verifying set allowed to chose these features, which gave the best results. Classifier performance measures obtained for unsupervised learning ART2 neural network was comparable with these reached for multiplayer perceptron structures.

Supervised and Unsupervised Learning Systems as a Part of Hybrid Structures Applied in EGG Signals Classifiers.

This paper aims at investigating an unsupervised learnt neural networks in classifier applications and comparing them to supervised perceptron type nets. The proposed solutions focus on combing the time-frequency preliminary analysis by means of wavelet transform with application of self organizing maps. Using wavelet transform as a feature extraction tool allowed to reveal important parameters included both in time and frequency domain of non-stationary electrogastrographic signals, which were classified in elaborated systems. Proposed structures were tested using the set of clinically characterized EGG signals of 62 patients, as cases with different level rhythm disturbances from bradygastria up to tachygastria together with some artifacts of non-stationary character such as muscle thrill etc. Additionally similar control group of healthy patients was analyzed. The results of the proposed methodology are illustrated in the measure of sensitivity and specificity, where the best classifier based on Kohonen maps with preliminary wavelet processing reached the performance above 90%.

An improvement of unsupervised hybrid biomedical signal classifiers by optimal feature extraction in system preliminary layer.

In this paper we try to place emphasis especially on the feature extraction stage of classification procedure, where new feature vectors obtained from a high-dimensional data space, which the best match the analysed classification task are proposed. Based on multilevel Mallat wavelet decomposition, parameters obtained directly from the wavelet component as well as feature resulting from energy and entropy analysis are tested. In classifier part of proposed hybrid systems, unsupervised learning systems with self organizing maps (SOM) and adaptive resonance networks (ART2) are verified. T-F methods and particularly wavelet analysis was chosen as feature extraction tool because of its ability to deal with non-stationary signals. It is important to take into consideration, that heart rate variability (HRV) signals, which were classified in elaborated systems are nonstationary and have important parameters included both in time and frequency domain. Proposed structures were tested using the set of clinically characterized heart rate variability (HRV) signals of 62 patients, as cases with a coronary artery disease of different level. Additionally similar control group of healthy patients was analyzed. Whole database was divided into learning and verifying set. Results showed, that the new HRV signal representation obtained in the space created by the feature vector based on Shannon entropy of Mallat component energy distribution gave the best classifier performance with ART2 neural structure used in classifier part of described hybrid system.

Neuropeptide Y in obese women during treatment with adrenergic modulation drugs.

BACKGROUND: The aim of the study is the assessment whether weight loss treatment with adrenergic modulation drugs modifies neuropeptide Y (NPY) plasma concentration in obese women. MATERIAL AND METHODS: 13 obese women (BMI 38.3 +/- 4.4) were tested before and subsequently 10 and 20 days after weight loss treatment. The treatment consisted of a very low caloric diet of 400 kcal (1670 kJ) daily combined with ephedrine with caffeine (E + C) or ephedrine with caffeine and yohimbine (E + C + Y) administered for 10 days using the cross-over method. The patients underwent physical examination, including heart rate and blood pressure measurements, spectral heart rate variability (HRV) at rest and after 3 minute handgrip and a 15 minute cycloergometer exercise at 75 W. All the above mentioned tests were carried out thrice in each patient. In 13 obese patients and in 6 control women plasma NPY concentrations were determined by a specific radioimmunoassay using rabbit anti-NPY antiserum and a standard synthetic porcine NPY (Peninsula Lab.). RESULTS: Plasma NPY concentrations were significantly lower in the obese persons compared with the control group. During weight loss treatment with adrenergic modulation drugs no changes in plasma NPY were found at rest and after physical exercise. Also no differences in HRV indices were observed. CONCLUSIONS: 1. Low plasma NPY concentration observed in obesity may be a contraregulatory factor that could prevent further weight increase. 2. Weight reduction treatment did not affect plasma NPY concentration and cardiovascular response to physical exercise. 3. The doses of adrenergic modulation drugs used in our study did not induce any serious side effects, and were so low that no change of plasma NPY concentration and cardiovascular responses were observed at rest.