The Use of Continuous Wavelet Transform Based on the Fast Fourier Transform in the Analysis of Multi-channel Electrogastrography Recordings.

This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal.

The extraction of the new components from electrogastrogram (EGG), using both adaptive filtering and electrocardiographic (ECG) derived respiration signal.

Electrogastrographic examination (EGG) is a noninvasive method for an investigation of a stomach slow wave propagation. The typical range of frequency for EGG signal is from 0.015 to 0.15 Hz or (0.015-0.3 Hz) and the signal usually is captured with sampling frequency not exceeding 4 Hz. In this paper a new approach of method for recording the EGG signals with high sampling frequency (200 Hz) is proposed. High sampling frequency allows collection of signal, which includes not only EGG component but also signal from other organs of the digestive system such as the duodenum, colon as well as signal connected with respiratory movements and finally electrocardiographic signal (ECG). The presented method allows improve the quality of analysis of EGG signals by better suppress respiratory disturbance and extract new components from high sampling electrogastrographic signals (HSEGG) obtained from abdomen surface. The source of the required new signal components can be inner organs such as the duodenum and colon. One of the main problems that appear during analysis the EGG signals and extracting signal components from inner organs is how to suppress the respiratory components. In this work an adaptive filtering method that requires a reference signal is proposed. In the present research, the respiratory component is obtained from non standard ECG (NSECG) signal. For purposes of this paper non standard ECG (namely NSECG) is used, because ECG signal was recorded by other than the standard electrodes placement on the surface of the abdomen. The electrocardiographic derived respiration signal (EDR) is extracted using the phenomena of QRS complexes amplitude modulation by respiratory movements. The main idea of extracting the EDR signal from electrocardiographic signal is to obtain the modulating signal. Adaptive filtering is done in the discrete cosine transform domain. Next the resampled HSEGG signal with attenuated respiratory components is low pass filtered and as a result the extended electrogastrographic signals, included EGG signal and components from other inner organs of digestive system is obtained. One of additional features of the proposed method is a possibility to obtain simultaneously recorded signals, such as: non-standard derivation of ECG, heart rate variability signal, respiratory signal, and EGG signal that allow investigating mutual interferences among internal human systems.

The relationship between clinical measurements and gait analysis data in children with cerebral palsy.

Spasticity is a common impairment that interferes with motor function (particularly gait pattern) in children with cerebral palsy (CP). Gait analysis and clinical measurements are equally important in evaluating and treating gait disorders in children with CP. This study aimed to explore the relationship between the spasticity of lower extremity muscles and deviations from the normal gait pattern in children with CP. Thirty-six children with spastic CP (18 with spastic hemiplegia [HS] and 18 with spastic diplegia [DS]), ranging in age from 7 to 12 years, participated in the study. The children were classified as level I (n=24) or level II (n=12) according to the Gross Motor Function Classification System. Spasticity levels were evaluated with the Dynamic Evaluation of Range of Motion (DAROM) using the accelerometer-based system, and gait patterns were evaluated with a three dimensional gait analysis using the Zebris system (Isny, Germany). The Gillette Gait Index (GGI) was calculated from the gait data. The results show that gait pathology in children with CP does not depend on the static and dynamic contractures of hip and knee flexors. Although significant correlations were observed for a few clinical measures with the gait data (GGI), the correlation coefficients were low. Only the spasticity of rectus femoris showed a fair to moderate correlation with GGI. In conclusion, the results indicate the independence of the clinical evaluation and gait pattern and support the view that both factors provide important information about the functional problems of children with CP.

The usefulness of accelerometric registration with assessment of tremor parameters and their symmetry in differential diagnosis of parkinsonian, essential and cerebellar tremor.

BACKGROUND AND PURPOSE: The aim of the study was to perform an analysis of the recorded tremor using accelerometry and select those parameters that are the most useful in differentiation of tremor types. MATERIAL AND METHODS: We examined 45 patients with parkinsonian tremor (PT), 39 patients with essential tremor (ET) and 35 patients with cerebellar tremor (CT). The control group consisted of 52 healthy persons. The analysis included tremor intensity, frequency of spectral peaks, centre frequency, standard deviation of the centre frequency, and harmonic index. Parameters of tremor were compared between particular groups of patients with pathological tremor and with the control group. The side-to-side symmetry of these parameters was also analysed. RESULTS: Tremor intensity was significantly higher in patients than in controls. There was a significant side-to-side asymmetry of intensity in all patient groups. Significantly lower peak frequency, centre frequency and standard deviation of centre frequency were found in patients compared to the control group. The frequency was symmetric in ET and in controls, but asymmetric in other subjects. The differences between hands regarding the standard deviation of centre frequency were significantly greater in all patient groups than in controls, who revealed no difference of this parameter between sides. Harmonic index was significantly greater and asymmetric in all groups of patients when compared to the control group. CONCLUSIONS: Standard deviation of centre frequency and harmonic index are the most valuable variables in differentiation of tremor. The assessment of symmetry of tremor parameters is useful in discrimination of various types of pathological tremor.

Essential tremor - assessment of tremor accelerometric parameters' symmetry and the relationship between hand dominance and severity of tremor.

BACKGROUND AND PURPOSE: Essential tremor (ET) is likely the most common movement disorder. The aim of the study was to carry out spectral analysis of the essential tremor recorded by an accelerometer and assess the symmetry of tremor parameters between the two hands. MATERIAL AND METHODS: We examined 39 patients with ET diagnosed clinically using the criteria of the Movement Disorder Society. The control group consisted of 52 healthy persons. A biaxial accelerometer mounted at the dorsal side of the hand was used. Spectral analysis was performed. Tremor intensity, frequency of spectral peaks, centre frequency, standard deviation of the centre frequency, and harmonic index were measured. The side-to-side symmetry of these parameters was analysed. The relationship of hand dominance and severity of tremor was also analysed. RESULTS: There was significant side-to-side asymmetry of intensity in ET. The intensity in the more affected hand was over two times higher than that in the less affected one. The tremor was more severe in the nondominant hand in 62% of patients. In spite of significant difference in tremor intensity between the two sides, tremor frequency was similar in both hands. The standard deviation of centre frequency was significantly lower and the harmonic index was significantly higher in the more trembling hand. CONCLUSIONS: Accelerometric registration revealed that asymmetry of intensity and symmetry of frequency are characteristic features of ET. The remaining two coefficients reflecting the rhythmicity and regularity of tremor also differed considerably between the hands.

Analysis of selected parameters of tremor recorded by a biaxial accelerometer in patients with parkinsonian tremor, essential tremor and cerebellar tremor.

BACKGROUND AND PURPOSE: Accelerometric registration of tremor with the subsequent estimation of its parameters allows objective collection of information. The aim of the study was an analysis and visualization of the signal of the recorded tremor, including assessment of spectral components at the dominant frequency and selection of those parameters that are most useful in differentiation of tremor types. MATERIAL AND METHODS: We examined 44 patients with parkinsonian tremor (PT), 39 patients with essential tremor (ET), and 13 patients with cerebellar tremor (CT), diagnosed clinically using the criteria of the Movement Disorders Society and the Tremor Investigation Group. The control group consisted of 26 healthy persons. A biaxial accelerometer mounted at the dorsal surface of the hand was used. Spectral analysis was performed. The analysis focused on determination of spectrum shape, frequency of spectral peaks, centre frequency, standard deviation of the centre frequency, harmonic index, as well as changes in frequency over time. RESULTS: In patients with pathological tremor (PT, ET, and CT), a single spectral peak was observed that persisted for the whole registration period. In the control group, tremor was characterized by a wide range of spectral frequencies with marked variability in time. Statistical analysis revealed significantly lower frequencies of the highest peak and of the central frequency, a significantly smaller standard deviation of the central frequency and a significantly higher harmonic index in cases of pathological tremors as compared to the control group. CONCLUSIONS: The presented method enables distinction between pathological and physiological tremor and helps in differentiation of various types of pathological tremor.