Alexander Tataraidze

Application of step-frequency radars in medicine

The paper summarizes results of step-frequency radars application in medicine. Remote and non-contact control of physiological parameters with modern bioradars provides a wide range of possibilities for non-contact remote monitoring of a human psycho-emotional state and physiological condition. The paper provides information about technical characteristics of bioradars designed at Bauman Moscow State Technical University and experiments using them. Results of verification experiment showed that bioradars of BioRASCAN type may be used for simultaneous remote measurements of breathing and heart rate parameters. In addition, bioradar assisted experiments for detecting of different sleep disorders are described. Their results proved that method of bioradiolocation allows correct estimation of obstructive sleep apnea severity compared to the polysomnography method, which satisfies standard medical recommendations.

Bioradiolocation-based sleep stage classification

This paper presents a method for classifying wakefulness, REM, light and deep sleep based on the analysis of respiratory activity and body motions acquired by a bioradar. The method was validated using data of 32 subjects without sleep-disordered breathing, who underwent a polysomnography study in a sleep laboratory. We achieved Cohens kappa of 0.49 in the wake-REM-light-deep sleep classification, 0.55 for the wake-REM-NREM classification and 0.57 for the sleep/wakefulness determination. The results might be useful for the development of unobtrusive sleep monitoring systems for diagnostics, prevention, and management of sleep disorders.This paper presents a method for classifying wakefulness, REM, light and deep sleep based on the analysis of respiratory activity and body motions acquired by a bioradar. The method was validated using data of 32 subjects without sleep-disordered breathing, who underwent a polysomnography study in a sleep laboratory. We achieved Cohens kappa of 0.49 in the wake-REM-light-deep sleep classification, 0.55 for the wake-REM-NREM classification and 0.57 for the sleep/wakefulness determination. The results might be useful for the development of unobtrusive sleep monitoring systems for diagnostics, prevention, and management of sleep disorders.

Comparison of Bioradiolocation and Respiratory Plethysmography Signals in Time and Frequency Domains on the Base of Cross-Correlation and Spectral Analysis

Comparison of bioradiolocation and standard respiratory plethysmography signals during simultaneous registration of different types of the human breathing movements is performed in both time and frequency domains. For all couples of synchronized signals corresponding to bioradiolocation and respiratory plethysmography methods, the cross-correlation and spectral functions are calculated, and estimates of their generalized characteristics are defined. The obtained results consider bioradiolocation to be a reliable remote sensing technique for noncontact monitoring of breathing pattern in medical applications.

Sleep stage classification based on respiratory signal.

One of the research tasks, which should be solved to develop a sleep monitor, is sleep stages classification. This paper presents an algorithm for wakefulness, rapid eye movement sleep (REM) and non-REM sleep detection based on a set of 33 features, extracted from respiratory inductive plethysmography signal, and bagging classifier. Furthermore, a few heuristics based on knowledge about normal sleep structure are suggested. We used the data from 29 subjects without sleep-related breathing disorders who underwent a PSG study at a sleep laboratory. Subjects were directed to the PSG study due to suspected sleep disorders. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. The accuracy of 77.85 ± 6.63 and Cohens kappa of 0.59 ± 0.11 were achieved for the classifier. Using heuristics we increased the accuracy to 80.38 ± 8.32 and the kappa to 0.65 ± 0.13. We conclude that heuristics may improve the automated sleep structure detection based on the analysis of indirect information such as respiration signal and are useful for the development of home sleep monitoring system.

A Novel Method for Recognition of Bioradiolocation Signal Breathing Patterns for Noncontact Screening of Sleep Apnea Syndrome

A novel method for recognition of breathing patterns of bioradiolocation signals breathing patterns (BSBP) in the task of noncontact screening of sleep apnea syndrome (SAS) is proposed and implemented on the base of wavelet transform (WT) and neural network (NNW) applications. Selection of the optimal parameters of WT includes determination of the proper level of wavelet decomposition and the best basis for feature extraction using modified entropy criterion. Selection of the optimal properties of NNW includes defining the best number of hidden neurons and learning algorithm for the chosen NNW topology. The effectiveness of the proposed approach is tested on clinically verified database of BRL signals corresponding to the three classes of breathing patterns: obstructive sleep apnea (OSA); central sleep apnea (CSA); normal calm sleeping (NCS) without sleep-disordered breathing (SDB) episodes.

Sleep architecture measurement based on cardiorespiratory parameters

This paper presents a method for the detection of wakeful state, rapid eye movement sleep (REM), light sleep (N1&N2) and deep sleep (N3&N4) based on cardiorespiratory parameters. Experiments were conducted with data of 625 subjects without sleep-disordered breathing selected from the SHHS dataset. Compared to previous studies, our method considers results of neighboring epochs classification and epoch position over record time. The method demonstrates Cohens kappa of 0.57 ± 0.13 and the accuracy of 71.4 ± 8.6 %. The results might contribute to the development of screening tools for diagnostics, prevention, and management of sleep disorders.This paper presents a method for the detection of wakeful state, rapid eye movement sleep (REM), light sleep (N1&N2) and deep sleep (N3&N4) based on cardiorespiratory parameters. Experiments were conducted with data of 625 subjects without sleep-disordered breathing selected from the SHHS dataset. Compared to previous studies, our method considers results of neighboring epochs classification and epoch position over record time. The method demonstrates Cohens kappa of 0.57 ± 0.13 and the accuracy of 71.4 ± 8.6 %. The results might contribute to the development of screening tools for diagnostics, prevention, and management of sleep disorders.

Sleep stage classification based on bioradiolocation signals.

This paper presents an algorithm for the detection of wakeful state, rapid eye movement sleep (REM) and non-REM sleep based on the analysis of respiratory movements acquired through a bioradar. We used the data from 29 subjects without sleep-related breathing disorders who underwent a polysomnography study at a sleep laboratory. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. Cohens kappa of 0.56 ± 0.16 and accuracy of 75.13 ± 9.81 % were achieved when compared to polysomnography results. The results of our work contribute to the development of home sleep monitoring systems.

Estimation of respiratory rhythm during night sleep using a bio-radar

An assessment of bio-radiolocation monitoring of respiratory rhythm during sleep is given. Full-night respiratory inductance plethysmography (RIP) and bio-radiolocation (BRL) records were collected simultaneously in a sleep laboratory. Polysomnography data from 5 subjects without sleep breathing disorders were used. A multi-frequency bioradar with step frequency modulation was applied. It has 8 operating frequencies ranging from 3.6 to 4.0 GHz. BRL data are recorded in two quadratures. Respiratory cycles were detected in time domain. Obtained data was used for the evaluation of correlation between BRL and RIP respiration rate estimates. Strong correlation between corresponding time series was revealed. BRL method is reliably implemented for estimation of respiratory rhythm and respiratory rate variability during full night sleep.

Bioradiolocation as a Technique for Remote Monitoring of Vital Signs

8.

Estimation of a priori probabilities of sleep stages: A cycle-based approach

This paper presents a model for the estimation of a priori probabilities of sleep epoch classes based on the epoch location in a sleep cycle. These probabilities are used as additional features for sleep stage classification based on the analysis of respiratory effort. The model was validated with data of 685 subjects selected from the Sleep Heart Health Study dataset. The model improves a base algorithm by 8 percent points and demonstrates Cohens kappa of 0.56 ± 0.12. Our results will contribute to the development of screening tools for unobtrusive sleep structure estimation.