Piotr Przystup

A multisensor detector of a sleep apnea for using at home

Diagnosis of obstructive sleep apnea usually involves polysomnographic analysis, which unfortunately requires overnight stay in a specialized clinic and is very uncomfortable for a patient. This paper describes the method and apparatus for recording a set of signals to detect sleep apnea. The device records the following signals simultaneously: three-channel ECG, respiratory functions, signals from the accelerometer, and snoring sounds. Measurements are carried out during the night, in the patients home. Algorithm for automatic analysis of the data can be used to detect sleep apnea and hypopnea.

ECG-based prediction of ventricular fibrillation by means of the PCA

A Sudden Cardiac Death (SCD) is a death resulting from cardiac failure with no significant symptoms earlier than one hour before occurrence. It is the cause of for approximately 400000 deaths per year in United States and millions of deaths worldwide. The proposed system, including two-stage algorithm and wearable diagnostic device allows for SCD risk estimation and continuous monitoring of high risk patients. A single channel of the ECG is analyzed. A classification of the patients into normal and high risk group is based on the evaluation of a QRS complex by means of the PCA. The algorithm has been tested using Sudden Cardiac Death Holter Database.

Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal

Obstructive sleep apnea (OSA) is a condition of cyclic, periodic obstruction (stenosis) of the upper respiratory tract. OSA could be associated with serious cardiovascular problems, such as hypertension, arrhythmias, hearth failure or peripheral vascular disease. Understanding the way of connection between OSA and cardiovascular diseases is important to choose proper treatment strategy. In this paper, we present a method for integrated measurements of biosignals for automatic OSA detection. The proposed method was implemented using a portable device with the application of the Support Vector Machine (SVM) classifier. The specific objective of this work is to analyze the minimum set of features for the ECG signal that could produce acceptable classification results. Those features can be further expanded using other biosignals, measured by the portable SleAp device. Additionally, the influence of the body movements and positions on measurement results with SleAp system are presented. The proposed system could help to determine the influence of OSA on the state of the cardiovascular system.

Self diagnostics using smart glasses - preliminary study

In this preliminary study we analyzed the possibility of the reliable measurement of biomedical signals with some potential hardware extensions of smart glasses. Using specially designed experimental prototypes four category of biomedical signals were measured: electrocardiograms, electromyograms, electroencephalograms and respiration waveforms. Experiments with volunteers proved that using even simple construction of sensors it is possible to reliable measure biomedical signals with the quality enough for screening purposes as for the needs of simple interaction between an user and smart glasses.

The role of EMG module in hybrid interface of prosthetic arm

Nearly 10% of all upper limb amputations concern the whole arm. It affects the mobility and reduces the productivity of such a person. These two factors can be restored by using prosthetics. However, the complexity of human arm makes restoring its basic functions quite difficult. When the osseointegration and/or targeted muscle reinnervation (TMR) are not possible, different modalities can be used to control the prosthesis. In this paper the usability of electromyography (EMG) signals for such a control is evaluated. Method: first, the types of operations performed by the prosthetic arm that could be handled by EMG module were defined. The raw EMG signal, corresponding to the predefined gesture, was acquired from the surface of trapezius muscle. The pattern recognition neural network was trained to classify gestures based on recorded RAW data. Results: The neural network was trained using 56 signals corresponding to performed gestures. Optimal performance was achieved for 29 training cycles. The network was tested using data set of 56 gestures. The designed network was tested on gestures recorded from 10 volunteers. The gestures were correctly classified with nearly 84% accuracy. Conclusions: The EMG analysis is a reliable modality when it comes to hybrid interfaces for control over prosthetic arm.

A body position influence on ECG derived respiration

An influence of a human body position on ECG derived respiration (EDR) signal is presented in the paper. Examinations were performed during deep, suspended and normal breathing for eight people in four different body positions. EDR and thoracic impedance signals were compared using correlation and standard deviation coefficients. Obtained results have shown that it is possible to monitor breath activity of people being in different position, however a precise interpretation of the obtained signal is limited.

Cardiovascular data analysis using electronic wearable eyeglasses - preliminary study

The paper presents an alternative approach to the monitoring of the cardiovascular system. The study depicts configurations of the utilized system and preliminary results of electrical and mechanical parameters of the cardiac system which can be measured using a head-worn device.

Multichannel Human Body Communication

Human Body Communication is an attractive alternative for traditional wireless communication (Bluetooth, ZigBee) in case of Body Sensor Networks. Low power, high data rates and data security makes it ideal solution for medical applications. In this paper, signal attenuation for different frequencies, using FR4 electrodes, has been investigated. Performance of single and multichannel transmission with frequency modulation of analog signal has been tested. Experiment results show that HBC is a feasible solution for transmitting data between BSN nodes.