Andrius Petrenas

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.

Low-complexity detection of atrial fibrillation in continuous long-term monitoring

This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief AF episodes. Despite its very simple structure, the results show that the detector performs better on the MIT-BIH Atrial Fibrillation database than do existing detectors, with high sensitivity and specificity (97.1% and 98.3%, respectively). The detector can be implemented with just a few arithmetical operations and does not require a large memory buffer thanks to the short window.

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

Parametrization of Physical Activity Aggregation.

100\,\upmu \hbox {V}

Ventricular activity cancellation in ECG using an adaptive echo state network

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.

Digital filter in hardware loop for on line ECG signal baseline wander reduction

This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate. Artificial neural network with either linear and non-linear outputs was investigated as a feature classifier. PhysioNet databases, namely, the MIMIC II and the MIMIC, were used for training and testing, respectively. After annotating the PPGs with respect to synchronously recorded electrocardiogram, two main types of PVCs were distinguished: with and without the observable PPG pulse. The obtained sensitivity and specificity values for both considered PVC types were 92.4/99.9% and 93.2/99.9%, respectively. The achieved high classification results form a basis for a reliable PVC detection using a less obtrusive approach than the electrocardiography-based detection methods.

A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation

This work introduces a novel approach to parametrization of physical activity profile. The proposed parameter, named as physical activity aggregation, is useful for evaluating a distribution of daily or weekly physical activity. The parameter takes a large value for a highly accumulated physical activity, whereas is much lower for an evenly spread activity over the monitoring period. The parameter was investigated on step data obtained using a smart wristband on a group of 71 participants with cardiovascular disease. The results of the pilot study show that the proposed parameter is capable of discriminating among different physical activity profiles, including sedentary behaviour, going to and from work, walking in a park and being active the entire day. Moreover, the results demonstrate the tendency that middle-aged and older women are associated with lower aggregation values, suggesting that they probably spend less time in sedentary behaviour compared to men of the same age. The proposed parameter has potential to be useful for characterizing physical activity profile, as well as, for investigating its relation to health outcomes, e.g., during ambulatory rehabilitation after major cardiovascular events.