Arunas Lukosevicius

A comparison of conductive textile-based and silver/silver chloride gel electrodes in exercise electrocardiogram recordings

BACKGROUND The goal of this study was to compare disposable silver/silver chloride and reusable conductive textile-based electrodes in electrocardiogram (ECG) signal monitoring during physical activity. MATERIALS AND METHODS The reusable electrodes were produced using thin silver-plated nylon 117/17 2-ply conductive thread (Statex Productions & Vertriebs GmbH, Bremen, Germany) sewed with a sewing machine on a chest belt. The disposable and reusable electrodes were compared in vivo according to ECG signal baseline drift, broadband electrode noise properties, and influence of electrode area to ECG signal morphology and frequency content. Twelve volunteers were included in this study. RESULTS Electroconductive textile-based ECG electrodes produce significantly more noise in a very low frequency band (0-0.67 Hz) and not significantly less of broadband noise (0-250 Hz) than disposable silver/silver chloride electrodes. Decreasing area of textile electrodes decreases fidelity of registered ECG signals at low frequencies. CONCLUSION Textile electrodes having adequate area can be used in more applications than only R-R interval monitoring.

An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation

A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in recordings with two or more leads. The method is based on an echo state neural network which estimates the time-varying, nonlinear transfer function between two leads, one lead with atrial activity and another lead without, for the purpose of canceling ventricular activity. The network has different sets of weights that define the input, hidden, and output layers, of which only the output set is adapted for every new sample to be processed. The performance is evaluated on ECG signals, with simulated f-waves added, by determining the root mean square error between the true f-wave signal and the estimated signal, as well as by evaluating the dominant AF frequency. When compared to average beat subtraction (ABS), being the most widely used method for QRST cancellation, the performance is found to be significantly better with an error reduction factor of 0.24-0.43, depending on f-wave amplitude. The estimates of dominant AF frequency are considerably more accurate for all f-wave amplitudes than the AF estimates based on ABS. The novel method is particularly well suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest.

Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare

Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.

Model based investigation of retinal vessel tortuosity as a function of blood pressure: preliminary results

Tortuosity is one of parameters which describe a state of the eye fundus blood vessels. Tortuosity can be estimated from the detected vessels in optical fundus images. The increase in vessel tortuosity was observed in eyes of patients with advanced background diabetic retinopathy, papilloedema, even in some completely healthy eyes (in this case tortuosity does not change in time). Though many methods to estimate eye vessel tortuosity exist, dependencies between tortuosity and parameters of cardiovascular system are not fully explored. In this paper we studied whether different tortuosity estimation algorithms can detect the change of blood pressure in the cylindrical segment of the vessel modeled using finite elements method. In addition we studied how does one inhomogeneity added inside the blood vessel influence the tortuosity and what are the relationships between the different tortuosity estimates and blood pressure? We found that even single inhomogeneity of the vessel wall triggers the increase of tortuosity when inner blood pressure increases. The resulting dependencies among different tortuosity estimates and blood pressure are mostly nonlinear.

Detection of occult paroxysmal atrial fibrillation

This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to

Ultrasound Medical Diagnostics Laboratory for Remote Learning in EVICAB*Campus

100\,\upmu \hbox {V}

Video and inertial sensors based estimation of kinematical parameters in swimming sport

100μV RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.

Ventricular activity cancellation in ECG using an adaptive echo state network

This paper presents a unified approach to multiscale detection of transient evoked otoacoustic emissions (TEOAEs). Using statistical detection theory, it is shown that the optimal detector involves a time windowing operation where the window can be estimated from ensemble correlation information. The detector performs adaptive splitting of the signal into different frequency bands using either wavelet or wavelet packet decomposition. A simplified detector is proposed in which signal energy is omitted. The results show that the simplified detector performs significantly better than existing TEOAE detectors based on wave reproducibility or the modified variance ratio, whereas the detector involving signal energy does not offer such a performance advantage.