Dariusz Komorowski

Preliminary Investigations Regarding the Blood Volume Estimation in Pneumatically Controlled Ventricular Assist Device by Pattern Recognition

Mechanical heart supporting is one of possible therapies in case of circulatory system insufficiency. It is realized by application of various types of Ventricular Assist Devices (VAD). For monitoring, optimizing and automatic control of heart supporting process it is necessary to measure blood volume pumped through the VAD during single cycle. The paper presents a novel approach to the problem of reliable VAD’s output estimation by the use of membrane’s shape pattern recognition and related blood’s chamber volume determination. Among many possibilities, the application of pattern recognition system has been decided leading to the promising results and making possible implementation of the automatic control algorithm in the future for the whole measurement process.

The wireless system for EGG signal acquisition

The paper provides a case study on the wireless circuit for the purpose of the EGG (Electrogastrography) signal acquisition. The authors present an amplifier based on the ADS1298, wireless communication module (Bluetooth) and the microprocessor unit which have been recently built. The system is currently under examination but the outcome is very promising because the amplifier input is fed with the pure signal, i.e. no high/low pass filter is applied, that is a novelty. The system presented in this paper could be helpful in diagnosis gastric disorders in noninvasive way. It could determine patients with unexplained nausea, vomiting and other dyspeptic symptoms. The unit is under clinical trials tests and preliminary evaluation indicates acceptance by medical staff. Additional advantages are the relatively low cost of manufacture and the possibility of application remotely (pervasive computing).

The Use of Continuous Wavelet Transform Based on the Fast Fourier Transform in the Analysis of Multi-channel Electrogastrography Recordings

This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal.

The simultaneous recording and analysis both EGG and HRV signals

This paper presents a method for synchronous recording and analyzing both the electrogastrographic signal (EGG) and the heart rate variability signal (HRV). The electrogastrographic examination can be considered as a noninvasive method for an investigation of a stomach slow wave propagation. The four channel signal are non-invasively captured by the appropriately placed electrodes on the surface of the stomach. The EGG and electrocardiographic (ECG) signals, recorded simultaneously by means of the same electrodes and an amplifier, are separated by the proper digital filtration. In our work the EGG and ECG analysis is limited to calculation the most frequently used parameters: the dominant frequency of the EGG (DF), and LF/HF ratio of the HRV power spectrum for the ECG. In this way it is possible to examine mutual interaction among EGG and HRV. This paper also depicts the preliminary results of a comparison of the some EGG and the HRV parameters e.g. dominant power distribution (DPD) and balance of low frequency and high frequency of HRV power spectrum.

The noise influence on determination dominant frequencies of EGG signal

Electrogastrographic examination (EGG) can be considered as a noninvasive method for an investigation of a stomach slow wave propagation This paper presents a method for determining dominant frequencies. It also shows details of influence of a noise on dominant frequencies determination. The EGG signal is noninvasively captured by appropriately placed electrodes on the surface of the stomach. The typical range of frequency for EGG signal is from 0.015Hz to 0.15Hz. One of EGG signal analyzing method is based on a determination of dominant harmonic frequencies contained in the chosen segments of EGG signal. The dominant frequencies are used for calculation base parameters of the EGG signal.

Pressure and Output Flow Estimation of Pneumatically Controlled Ventricular Assist Device (VAD) with the help of Both Acceleration and Gyro Sensors

The Polish ventricular assist device (VAD) POLVAD is a pulsatile pump designated to both short and mid term, single and/or double chamber circulatory system assistance during cardiac surgery treatment. Also, it can be applied as temporary assistance of critical heart failure up to its energetic recovery or homodynamic sufficiency (before or post operation stages) or as a temporary bridge to heart transplantation. The important parameters necessary to know during such described assistance are both value and shape of blood pressure and flow. These parameters are strongly dependent on shape of circulatory system input blood pressure curve as well as on set of physiological system parameters. Direct measurement of pressure and flow curves inside VAD is possible but unfortunately it is connected with additional risk for patient and require some construction changes or improvements of VAD itself. One of the possible solution allowing to satisfactory estimation of pressure and flow curve shape concerns application of sensors detecting movement of assistance chamber flexible elements. Therefore the application of monolithic acceleration and gyro sensors estimating both displacement and rotation of chamber construction elements caused by blood pressure changes seem to be reasonable and justified.

The First Experience with the Use of Noise-Assisted Empirical Mode Decomposition Algorithm in the Processing of Multi-channel Electrogastrography Signals

The electrogastrographic examination (EGG) is a non invasive method for the investigation of the myoelectrical stomach activities that is performed with the electrodes placed on the skin of the patient’s abdomen. The main aim of EGG examination is the assessment of gastric slow wave propagation. Observation and measurement of the wave parameters changes (e.g. amplitude and frequency) facilitates diagnosing certain abnormalities and disorders associated with the impaired gastric activity. Such abnormalities are usually difficult to detect with the traditional diagnostic methods. One of the main tasks of this study is to extract the gastric slow wave, occurring naturally in the EGG signals, from the raw multi-channel EGG signal. In the presented work, in order to extract slow waves from multi-channel EGG, a modified method of the Multivariate Empirical Mode Decomposition (MEMD) called the Noise-Assisted Empirical Mode Decomposition (NA-MEMD) is proposed to apply. The use of NA-MEMD algorithm and Hilbert-Huang spectrum (HHS) seems to be the appropriate method for analysing non-linear and non-stationary signals and so is the multi-channel EGG.

A new method for attenuation of respiration artifacts in electrogastrographic (EGG) signals

Electrogastrography (EGG) is a test method designed for noninvasive assessment of gastric slow waves propagation. The EGG signal is obtained from the electrodes respectively arranged on the surface of the patients abdomen. A significant problem during recording of the EGG signal is the elimination of disturbances occurring during registration and unwanted components of other signals such as: components of electrocardiographic (ECG), baseline drift or respiratory disturbances. These components are generally present in the signals registered from the surface of the abdomen of the patient. Since EGG frequency components partly overlap with the frequency components of respiratory artifacts, conventional band-pass digital or analog filtering may cause distortion in electrogastrographic signal. In the paper a method for removing respiratory interference occurring during registration of EGG signal and the effect of filtration on selected parameters of EGG signal analysis is presented. Respiratory artifacts are removed through the use of adaptive filter working in the DCT domain. The applied adaptive filtering method involves the use of the signal including respiratory disturbances. This signal is recorded synchronously with the EGG signal using a thermistor placed near the nose of the patient.

Heart Rate Analysis in the EGG Examination

This paper presents a novel method for synchronous recording and analyzing both the electrogastrographic signal (EGG) and the heart rate variability signal (HRV). The four channel signals are non-invasively captured by six electrodes placed on the surface of the stomach. The EGG and electrocardiographic (ECG) signals, recorded simultaneously using the same electrodes and an amplifier, are separated by digital filtration. In our work the EGG and ECG analysis is limited to the calculation the most frequently used parameters: the dominant frequency (DF), dominant power (DP) of the power spectrum for the EGG, and LF/HF ratio of the HRV power spectrum for the ECG signals.

Hybrid Feature Vector Extraction in Unsupervised Learning Neural Classifier

Feature extraction and selection method as a preliminary stage of heart rate variability (HRV) signals unsupervised learning neural classifier is presented. Multi-domain, mixed new feature vector is created from time, frequency and time-frequency parameters of HRV analysis. The optimal feature set for given classification task was chosen as a result of feature ranking, obtained after computing the class separability measure for every independent feature. Such prepared a new signal representation in reduced feature space is the input to neural classifier based on introduced by Grosberg adaptive resonance theory (ART2) structure. Test of proposed method carried out on the base of 62 patients with coronary artery disease divided into learning and verifying set allowed to chose these features, which gave the best results. Classifier performance measures obtained for unsupervised learning ART2 neural network was comparable with these reached for multilayer perceptron structures