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Analysis of brain activities in language perception for individuals with different musical backgrounds can be based upon the study of multichannel electroencephalograhy (EEG) signals acquired in different external conditions. The present paper is devoted to the study of the relationship of mental processes and the perception of external stimuli related to the previous musical education. The experimental set under study included 38 individuals who were observed during perception of music and during listening to foreign languages in four stages, each of which was 5 min long. The proposed methodology is based on the application of digital signal processing methods, signal filtering, statistical methods for signal segment selection and active electrode detection. Neural networks and support vector machine (SVM) models are then used to classify the selected groups of linguists to groups with and without a previous musical education. Our results include mean classification accuracies of 82.9% and 82.4% (with the mean cross-validation errors of 0.21 and 0.22, respectively) for perception of language or music and features based upon EEG power in the beta and gamma EEG frequency bands using neural network and SVM classification models.

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments.

The dynamic structure of the EEG signal is characterized by segments of synchronization and desynchronization. In this paper, the frequency and duration of these segments were monitored during calm meditation and insight meditation in experienced and naive meditators. A newly developed methodology based on complex continuous wavelet coherence was used to estimate these parameters. The durations highly depend on frequency band and vary from 60 ms to 250 ms. A shorter duration and a lower frequency of synchronization were found for experienced meditators during both types of meditations for the real and the imaginary parts of the complex continuous wavelet coherence. The greatest duration differences were in the gamma band, which may be associated with handling attention during meditation, whereas the differences in the alpha band were most significant for frequency. Combining the two parameters resulted in the total duration of the synchronization, which has discriminative accuracy of up to 100% and appears to be a sensitive parameter of the length of training of meditators.

This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features extraction, their classification and visualization. Real data were acquired from 41 cycling rides of the same 11.48-km long route divided into 2460 segments of approximately 60 s. The data were recorded with a varying sampling period within the range of 1–22 s depending on the route profile. The pre-processing stage included preparatory analysis, filtering and resampling of the data to a constant sampling rate. The proposed algorithm includes the evaluation of the cross-correlation between the heart rate and the altitude gradient as recorded by a GPS satellite system. A Bayesian approach was then applied to classify the cycling segment features into two classes (specifying cycling up and down) with the classification accuracy better than 93 %. A comparison with other classification methods is presented in the paper as well. The results include the following relationships: (1) the heart rate and altitude gradient, which shared a positive correlation coefficient of 0.62; (2) the heart rate and speed, which shared a negative correlation coefficient of −0.72 over all of the analysed segments; and (3) the mean heart rate change delay (6.8–11.5 s) in relation to the changes in the altitude gradients associated with cycling up and down. The paper forms a contribution to the use of computational intelligence and visualization for data processing both in cycling and fitness physical activities as well.

This paper presents the contactless measuring of breathing using the MS Kinect depth sensor and compares the results obtained with records of breathing taken by polysomnography (PSG). We explore the methods of signal denoising, resampling, and spectral analysis of acquired data as well as feature extraction and their Bayesian classification. The proposed methodology was applied for analysis of the long-term monitoring of individuals who were observed simultaneously by PSG and MS Kinect in the sleep laboratory. After time synchronization of polysomnographic and MS Kinect video data, features were extracted from both signals and compared. The average error of the frequency while being evaluated by MS Kinect that was related to that obtained by PSG was 3.75 %. The mean accuracy of the Bayesian classification of features into two classes (i.e. wake or sleep) was 88.90 and 88.95 % for the PSG and MS Kinect measurements, respectively. The strong likeness of features supports the hypothesis that contactless techniques may represent a valid alternative to the present approach of sleep monitoring, thereby allowing data acquisition in the home environment as well.

This paper presents a novel method of gait recognition that uses the image and depth sensors of the Microsoft (MS) Kinect to track the skeleton of a moving body and allows for simple human---machine interaction. While video sequences acquired by complex camera systems enable very precise data analyses and motion detection, much simpler technical devices can be used to analyze video frames with sufficient accuracy in many cases. The experimental part of this paper is devoted to gait data acquisition from 18 individuals with Parkinson's disease and 18 healthy age-matched controls via the proposed MS Kinect graphical user interface. The methods designed for video frame data processing include the selection of gait segments and data filtering for the estimation of chosen gait characteristics. The proposed computational algorithms for the processing of the matrices acquired by the image and depth sensors were then used for spatial modeling of the moving bodies and the estimation of selected gait features. Normalized mean stride lengths were evaluated for the individuals with Parkinson's disease and those in the control group and were determined to be 0.38 and 0.53 m, respectively. These mean stride lengths were then used as features for classification. The achieved accuracy was >90 %, which suggests the potential of the use of the image and depth sensors of the MS Kinect for these applications. Further potential increases in classification accuracy via additional biosensors and body features are also discussed.

As is known, Alzheimer’s disease (AD) is associated with cognitive deficits due to significant neuronal loss. Reduced connectivity might be manifested as changes in the synchronization of electrical activity of collaborating parts of the brain. We used wavelet coherence to estimate linear/nonlinear synchronization between EEG samples recorded from different leads. Mutual information was applied to the complex wavelet coefficients in wavelet scales to estimate nonlinear synchronization. Synchronization rates for a group of 110 patients with moderate AD (MMSE score 10 to 19) and a group of 110 healthy control subjects were compared. The most significant decrease in mutual information in AD patients was observed on the third scale in the fronto-temporal area and for wavelet coherence within the same areas as for mutual information; these areas are preferentially affected by atrophy in AD. The new method used utilizes mutual information in wavelet scales and demonstrates larger discriminatory values in AD compared to wavelet coherence. Як відомо, хвороба Альцгеймера (ХА) пов’язана з прогресуючим когнітивним дефіцитом у результаті істотної загибелі нейронів. Зменшення міжнейронних зв’язків може проявлятись як зміни ступеню синхронізації електричної активності взаємодіючих мозкових структур. Ми використовували методику оцінки вейвлет-когерентності для оцінки лінійної або нелінійної синхронізації зразків ЕЕГ, відведених від різних локусів кори. Визначення індексів взаємної інформації використовувалося для оцінки нелінійної синхронізації згідно з комплексними вейвлет-коефіцієнтами за вейвлет-шкалами. Було порівняно ступені синхронізації ЕЕГ-активності в групі пацієнтів, що страждали на ХА помірної тяжкості (оцінки за MMSE від 10 до 19 балів), та в групі із 110 контрольних здорових суб’єктів. Найістотніші зменшення індексів взаємної інформації у пацієнтів із ХА спостерігалися по третій шкалі для фронто-темпоральної зони; зменшення вейвлет-когерентності відзначались у тих самих зонах, що й зміни взаємної інформації. Саме ці зони зазнають переважної атрофії при ХА. Використаний новий метод базується на оцінках взаємної інформації за вейвлет-шкалами та демонструє більшу дискримінаційну здатність в умовах ХА, аніж визначення вейвлет-когерентності.

This paper presents the analysis of dental cast images and new mathematical methods for their evaluation. While depth sensors of sophisticated scanners allow precise spatial data processing, it is possible to use a multi-camera system for direct data acquisition and recognition of selected objects as well. The main goal of this paper is the analysis of three-dimensional objects to enable numerical evaluation of measurements important for proposals of the appropriate treatment after dental operations. Methods presented include (1) computer analysis of a single de-noised and thresholded image used for detection of its components and (2) presentation of tools for three-dimensional modeling using a double camera system. The proposed algorithms allow semi-automatic evaluation of measurements between selected objects. The resulting general algorithm can be used both in biomedical applications and engineering to detect measurements in static image frames or videosequences.

This paper presents new methods of orthodontic body segmentation using digital records of their plaster cast models under different types of illumination. Selected light conditions are used for the data acquisition to provide more clearly defined contours of the image components. The preliminary stage of the data processing uses the circular Hough transform, digital de-noising, and a separation of the orthodontic objects from their backgrounds employing Otsu’s thresholding method. The region-growing method using multiple seed points in a convex hull is then applied. The proposed general method identifies the common boundary of two neighboring and overlapping orthodontic objects with results enabling the efficient segmentation of digital data and their analysis through the computer network.

In needle electromyography, general mathematical methods of pattern recognition and signal analysis can be used to detect motor unit potentials and to classify various types of muscle diseases. The goal of the paper is to contribute to various methods enabling discrimination of individuals with axonal neuropathy (a positive set) from normal cases (a control set) using signals acquired from muscle activities. Data from a control set of 104 individuals and a set of 76 patients were used to validate selected methods of their separation and classification. Different features in both the time and frequency domains were studied to obtain the most reliable results . This novel approach involves comparison of individual features based on adaptive signal thresholding, as well as their combination, using supervised sigmoidal neural networks for their classification. Specificity, sensitivity and accuracy of the features used to detect individuals with axonal neuropathy was analysed using the receiver operating characteristic curves and confusion analysis. An accuracy higher than 93 % was achieved for the given sets of individuals and the optimal criterion values. The proposed modified Willison amplitude together with statistical and spectral properties of signal components classified individuals into sets of healthy and neuropathic patients by artificial neural networks with sensitivity 96.1 % and sufficient accuracy in the wide range of criterion values.

Studies have shown slow healing of peripheral nerve injury in elderly patients. Carpal tunnel syndrome (CTS) is the most frequent compressive mononeuropathy, affecting mostly older people and females. Few studies have assessed electrophysiological differences between younger and older patients. We aimed to evaluate age-dependent differences in electrophysiological parameters preoperatively and postoperatively over a 100-day postoperative period. This retrospective study included 258 hands of patients who underwent conventional open-technique carpal tunnel syndrome surgery. Patients with paresthesia in the median nerve distribution or with impaired sensation or abnormal findings in sensory and motor median nerve conduction studies were enrolled. The age dependence of the preoperative values of distal motor latency, amplitude of the compound motor action potential and sensory conduction velocity was estimated using regression analysis. Statistically significant age dependence was found for the preoperative distal motor latency, compound motor action potential, amplitude and sensory conduction velocity. The repair of segmental demyelination was nearly twice as slow in the older group, at a 5 % significance level, even when comparing groups with the same preoperative distal motor latency. Analysis of preoperative nerve conduction parameters indicates that surgery for carpal tunnel syndrome is performed later in older patients.

Age-related changes in the EEG energy and spectral composition were examined in 17,722 healthy subjects (truck drivers), 20 to 70 years old. Linear correlations between age vs global EEG energy and spectral powers (SPs) of EEG frequency ranges were estimated by linear regression analysis. Significant dependences of the global EEG energy and SPs of all EEG rhythms on age (age-related decreases) were found (most significant for the alpha range). An age-related decline of the EEG energy may be, in part, explained by age-related generalized brain atrophy and increased thickness of the scalp. While similarity measures show an agerelated decrease of the correlation between different sources of EEG, activity phase cancellation should also be taken into account. Smaller decreases in the absolute SPs of the beta and gamma ranges together with a general drop in the overall EEG power result in a trend toward relatively higher normalized representation of these frequency components with aging. The highest significance of decline in the alpha range can be related to a slowing oscillation frequency of alpha generators toward the theta band.