Oldřich Vyšata

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human–machine interaction.

Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect

This paper presents a novel method of Bayesian gait recognition using Microsoft (MS) Kinect image and depth sensors and skeleton tracking in three-dimensional space. Although video sequences acquired by a complex camera system enable a very precise data analysis, it is possible to use much simpler technical devices to analyze video frames with sufficient accuracy for many applications. The use of the MS Kinect allows a simple 3-D modeling using its image and depth sensors for data acquisition, resulting in a matrix of 640 × 480 elements used for spatial modeling of a moving body. The experimental part of the paper is devoted to the study of three data sets: (i) 18 individuals with Parkinsons disease, (ii) 18 healthy age-matched controls, and (iii) 15 trained young individuals forming the second reference set. The proposed algorithm involves methods for the estimation of the average stride length and gait speed of individuals in these sets. Digital signal processing methods and Bayesian probability classification algorithms are then used for gait feature analysis to recognize individuals suspected of having Parkinsons disease. The results include the estimation of the characteristics of selected gait features for patients with Parkinsons disease and for individuals from the reference sets, presentation of decision boundaries, and comparison of classification efficiency for different features. The achieved accuracy of the probabilistic classification was 94.1%.

Anti-NMDA receptor antibodies in patients with a first episode of schizophrenia

Background Encephalitis with antibodies against N-methyl-D-aspartate receptor (NMDA-R) is classified as an autoimmune disorder with psychotic symptoms, which are frequently dominant. However, it remains unclear how frequently NMDA-R antibodies lead to a condition that mimics psychosis and first-episode schizophrenia. In our work, we investigated the presence of antibodies against NMDA-R in patients with first-episode psychosis (FEP) in comparison with healthy volunteers. Methods This study included 50 antipsychotic-naïve patients with FEP (including 21 women) and 50 healthy volunteers (including 21 women). The mean age of the patients was 27.4 (±7.4) years and that of the healthy controls was 27.0 (±7.3) years. Antibodies against NMDA-R in the serum were detected by immunofluorescence. Results None of the investigated patients with an FEP and none of the healthy controls showed positive antibodies against NMDA-Rs. Conclusion According to results of studies, a small proportion of patients with an FEP possess antibodies against NMDA-R. However, the extent to which this finding contributes to the etiopathogenesis of the response to antipsychotic medication and whether immunomodulatory therapy is indicated in these cases remains uncertain.

Towards an inclusive Parkinson's screening system

In this study, brain, and gait dynamic information were combined and used for diagnosis and monitoring of Parkinsons disease (the most important Neurodegenerative Disorder). Analysis of the information corresponding to a prescribed movement involving tremor, and the related changes in brain connectivity is novel and original. Analytically, developing a space-time nonlinear adaptive system which fuses brain and gait information algorithmically is proposed here for the first time. The overall dynamic system will be constrained by the clinical impressions of the patient symptoms embedded in a knowledge-based system. The entire complex constrained problem were solved to enable a powerful model for recognition and monitoring of Parkinsons disease and establishing appropriate rules for its clinical following up.

Change in the Characteristics of EEG Color Noise in Alzheimer’s Disease

Neurophysiological experiments support the hypothesis of the presence of critical dynamics of brain activity. This is also manifested by power law of electroencephalography (EEG) power spectra, which can be described by the relation 1/fα. This dependence is a result of internal interactions between parts of the brain and is probably required for optimal processing of information. In Alzheimer’s disease, changes in the functional organization of the brain occur, which may be manifested by changes in the α coefficient. We compared the average values of α for 19 electrodes in the resting EEG record in 110 patients with moderate to severe Alzheimer’s disease (Mini-Mental State Examination [MMSE] score = 10-19) with 110 healthy controls. Statistically, the most significant differences are present in the prefrontal areas. In addition to the prefrontal and frontal areas, the largest separation value in the evaluation of receiver operating characteristic (ROC) curves was recorded in the temporal area. The coefficient alpha has few false-positive results in the optimal operating point of the ROC curve, and is thereby highly specific for Alzheimer’s disease.

Multi-channel EEG signal segmentation and feature extraction

Signal analysis of multi-channel data form a specific area of general digital signal processing methods. The paper is devoted to application of these methods for electroencephalogram (EEG) signal processing including signal de-noising, evaluation of its principal components and segmentation based upon feature detection both by the discrete wavelet transform (DWT) and discrete Fourier transform (DFT). The self-organizing neural networks are then used for pattern vectors classification using a specific statistical criterion proposed to evaluate distances of individual feature vector values from corresponding cluster centers. Results achieved are compared for different data sets and selected mathematical methods to detect and to classify signal segments features. Proposed methods are accompanied by the appropriate graphical user interface (GUI) designed in the MATLAB environment.

Common yew intoxication: a case report

IntroductionTaxine alkaloids cause fatal poisoning, in particular due to the compound’s toxic effect on the cardiovascular apparatus.Case presentationWe describe the case of a 39-year-old Caucasian man with common yew intoxication for whom cardiopulmonary resuscitation using all available methods, although delayed and extended, was successful.ConclusionsExtended and delayed cardiopulmonary resuscitation can be used successfully to treat common yew intoxication.

The use of combined illumination in segmentation of orthodontic bodies

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.

Complex continuous wavelet coherence for EEG microstates detection in insight and calm meditation

Complex continuous wavelet coherence (WTC) can be used for non-stationary signals, such as electroencephalograms. Areas of the WTC with a coherence higher than the calculated optimal threshold were obtained, and the sum of their areas was used as a criterion to differentiate between groups of experienced insight-focused meditators, calm-focused meditators and a control group. This method demonstrated the highest accuracy for the real WTC parts in the frontal region, while for the imaginary parts, the highest accuracy was shown for the frontal occipital pairs of electrodes. In the frontal area, in the broadband frequency, both types of experienced meditators demonstrated an enlargement of the increased coherence areas for the real WTC parts. For the imaginary parts unaffected by the volume conduction and global artefacts, the most significant increase occurred for the frontal occipital pair of electrodes.

Cerebral Venous Sinus Thrombosis: Clinical Characteristics and Factors Influencing Clinical Outcome.

The frequency of patients diagnosed with cerebral venous sinus thrombosis (CVST) has increased due to the expanded use of noninvasive brain imaging methods. The aim of this study was to assess the correlations between the location and extent of venous sinus impairment, clinical presentation during the acute phase, recanalization, the presence of parenchymal lesions, and clinical outcome after 3 to 4 months in patients with CVST. In a retrospective study, clinical and magnetic resonance imaging data from a cohort of 51 consecutive patients with CVST (mean age 33.1 ± 15.4 years) were collected and analyzed. Good clinical outcome after 3 to 4 months, which was assessed using the modified Rankin scale, significantly negatively correlated with a thrombosis location in the left transverse, left sigmoid, or superior sagittal sinus (P = .022, P = .045, and P = .046, respectively) and positively correlated with recanalization (P = .048). The clinical outcome was significantly more favorable in the females with gender-specific risk factors than in the males (P = .029). In conclusion, successful recanalization substantially helps to achieve good clinical outcome in patients with CVST.