Mario Cifrek

A capacitive intrabody communication channel from 100 kHz to 100 MHz

Intrabody communication (IBC) uses the human body as a signal transmission medium. In the capacitive coupling IBC approach, the signal is transmitted through the body, and the signal return path is closed through the environment. The received signal level is affected by the orientation of the transmitter with respect to the receiver, the number of ground electrodes connected to the body, the size of the receiver ground plane, and the surrounding environment. In this paper, we present a characterization of the capacitive IBC channel in the frequency range from 100 kHz to 100 MHz, obtained using a network analyzer and a pair of baluns. In order to better understand the transmission path in the frequency range of interest, we analyze the intrabody channel transmission characteristics using different electrode arrangements, test persons, environments, and body positions and movements. The transmission gain increases with frequency for 20 dB/dec and depends on the transmitter to the receiver distance, and the electrode arrangements. For a proper IBC configuration, the variations of the environment, test persons, body positions, and movements affect the transmission gain less than 2 dB.

Intrabody Communication in Biotelemetry

Biotelemetry is remote monitoring, measuring and recording of a living organism’s function, activity or condition. Network of sensor nodes placed on or implanted inside the body of a subject is called Body Area Network (BAN). In this work we will describe the principles of a wireless body area network which uses the human body as a transmission medium, namely intrabody communication (IBC). We will describe the limitations set on the IBC systems, describe dielectric properties of the human body as a transmission medium, specify different ways of transmitting signals through the human body and compare characteristics of the IBC systems found in the literature.

Measurement and analysis of surface myoelectric signals during fatigued cyclic dynamic contractions

Abstract A method of surface myoelectric (ME) signal measurement and analysis, with the aim of evaluating muscle fatigue during cyclic dynamic contractions of quadriceps muscle (lower leg extension and flexion exercise on ‘leg-extension’ training device), was developed. As an indicator of muscle fatigue a change in the power spectrum median frequency (MF), calculated from the spectrogram, has been used. Our method considers the maximum median frequency values during each contraction. A slope of the regression line (Hz/min) that fits the maximum values of MF in a least-square sense was used as a fatigue index. Dependencies of the muscle fatigue index, as well as median frequencies at the beginning of the exercise on spectrogram analysis parameters were considered, and analysis parameters for reliable results were chosen. Values of the slope of the regression line and MF at the beginning of an exercise are in agreement with the notion that rectus femoris has higher muscle fibre conduction velocity than both vasti muscles.

Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease.

BackgroundThis paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network.MethodsFuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo.ResultsOut of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained.ConclusionOur neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification.Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patients dynamic assessment, as opposed to the solution that provides a static assessment of the patient.

Classification of asthma using artificial neural network

This paper presents a system for classification of asthma based on artificial neural network. A total of 1800 Medical Reports were used for neural network training. The system was subsequently tested through the use of 1250 Medical Reports established by physicians from hospital Sarajevo. Out of the aforementioned Medical Reports, 728 were diagnoses of asthma, while 522 were healthy subjects. Out of the 728 asthmatics, 97.11% were correctly classified, and the healthy subjects were classified with an accuracy of 98.85%. Sensitivity and specificity were assessed, as well, which were 97.11% and 98.85%, respectively. Our system for classification of asthma is based on a combination of spirometry (SPIR) and Impulse Oscillometry System (IOS) test results, whose measurement results were inputs to artificial neural network. Artificial neural network is implemented to obtain both static and dynamic assessment of the patients respiratory system.

Integrated software suite for diagnosis of respiratory diseases

Respiratory diseases can be very difficult to diagnose because their symptoms are sometimes very similar to each other. If we analyze asthma and COPD (Chronic Obstructive Pulmonary Disease), diseases which are targeted in our research, we come to the conclusion that early detection of airway impairment can greatly assist in an early diagnosis. In this paper we present an integrated software suite that can help doctors make correct diagnosis of diseases such as asthma and COPD, using lung function tests. The software is based on object oriented methodology. The parameters of spirometry, IOS (Impulse Oscillometry System) and body plethysmography, used in the diagnosis of respiratory disease, will be included in the neuro-fuzzy system in order to help the software suggest proper diagnosis of asthma, COPD or normal lung condition. In order to meet all the conditions that are necessary for the proper and complete diagnosis of respiratory diseases, there is also information about the symptoms, allergies and auscultation of the patient included. In cases where it is not possible to determine the diagnosis on the basis of symptoms, spirometry and IOS test, the software indicates BDT (bronchial dilation test) and BPT (bronchial provocation test), after which new tests are required for spirometry, IOS and body plethysmography in order to get a complete diagnosis.

The application of Hilbert–Huang transform in the analysis of muscle fatigue during cyclic dynamic contractions

Surface electromyography (sEMG) is a common technique used in the assessment of local muscle fatigue. As opposed to static contraction situations, sEMG recordings during dynamic contractions are particularly characterised by non-stationary (and non-linear) features. Standard signal processing methods using Fourier and wavelet based procedures demonstrate well known restrictions on time–frequency resolution and the ability to process non-stationary and/or non-linear time-series, thus aggravating the spectral parameters estimation. The Hilbert–Huang transform (HHT), comprising of the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), provides a new approach to overcome these issues. The time-dependent median frequency estimate is used as muscle fatigue indicator, and linear regression parameters are derived as fatigue quantifiers. The HHT method is utilised for the analysis of the sEMG signals recorded over quadriceps muscles during cyclic dynamic contractions. The results are compared with those obtained by the Fourier and wavelet based methods. It is shown that HHT procedure provides the most consistent and reliable assessment of spectral and derived linear regression parameters, given the time epoch width and sampling interval in the time domain. The suggested procedure successfully deals with non-stationary and non-linear properties of biomedical signals.

Classification of Asthma Utilizing Integrated Software Suite

The aim of this study was to investigate problems in detecting and diagnosing asthma and based on it, to develop method for classification of asthma. This method is implemented through integrated software suite developed to assist physicians in the analysis and interpretation of pulmonary function test results to improve detection, diagnosis and treatment of asthma. The software determines and classifies the asthma based on fuzzy rules and trained neural network. More than one thousand report samples were used for training data. A total of 289 patients, previously diagnosed with asthma or normal lung conditions by physicians, were tested with this tool. The software performed the classification of patients with asthma in 97.22% and with normal lung function in 98.61% cases in concordance with physician’s report.

Classification of Chronic Obstructive Pulmonary Disease (COPD) Using Integrated Software Suite

Chronic Obstructive Pulmonary Disease (COPD) is a respiratory disorder characterized by chronic and recurrent airflow obstruction, which increases airway resistance. About 75% of COPD patients do not have established diagnosis, most of them in mild degree, but also 4% in severe and 1% in very severe degree of COPD. The reason for that are slow progression of symptoms as cough and exercise intolerance, as well as development of disease in the elderly. Integrated software suite is developed to assist clinicians in the analysis and interpretation of pulmonary function tests data to better detect, diagnose and treat COPD conditions. A total sum of 385 patient reports with previously diagnosed COPD or normal lung conditions by clinicians was tested with this tool. With diagnosed COPD by clinicians there were 252 patients, and even in 92% the software has performed the classification of COPD in the same way as doctors. The software classification of patients with normal lung function was 90.97%.

Calibration of 3D kinematic systems using orthogonality constraints

Processing images acquired by multi-camera systems is nowadays an effective and convenient way of performing 3D reconstruction. The basic output, i.e. the 3D location of points, can easily be further processed to also acquire information about additional kinematic data: velocity and acceleration. Hence, many such reconstruction systems are referred to as 3D kinematic systems and are very broadly used, among other tasks, for human motion analysis. A prerequisite for the actual reconstruction of the unknown points is the calibration of the multi-camera system. At present, many popular 3D kinematic systems offer so-called wand calibration, using a rigid bar with attached markers, which is from the end user’s point of view preferred over many traditional methods. During this work a brief criticism on different calibration strategies is given and typical calibration approaches for 3D kinematic systems are explained. In addition, alternative ways of calibration are proposed, especially for the initialization stage. More specifically, the proposed methods rely not only on the enforcement of known distances between markers, but also on the orthogonality of two or three rigidly linked wands. Besides, the proposed ideas utilize common present calibration tools and shorten the typical calibration procedure. The obtained reconstruction accuracy is quite comparable with that obtained by commercial 3D kinematic systems.