Siniša Sovilj

ECG Based Prediction of Atrial Fibrillation Development Following Coronary Artery Bypass Grafting

In patients undergoing coronary artery bypass grafting (CABG) surgery, post-operative atrial fibrillation (AF) occurs with a prevalence of up to 40%. The highest incidence is seen between the second and third day after the operation. Following cardiac surgery AF may cause various complications such as hemodynamic instability, heart attack and cerebral or other thromboembolisms. AF increases morbidity, duration and expense of medical treatments. This study aims at identifying patients at high risk of post-operative AF. Early prediction of AF would provide timely prophylactic treatment and would reduce the incidence of arrhythmia. Patients at low risk of post-operative AF could be excluded on the basis of the contraindications of anti-arrhythmic drugs. The study included 50 patients in whom lead II electrocardiograms were continuously recorded for 48 h following CABG. Univariate statistical analysis was used in the search for signal features that could predict AF. The most promising ones identified were P wave duration, RR interval duration and PQ segment level. On the basis of these, a nonlinear multivariate prediction model was made by deploying a classification tree. The prediction accuracy was found to increase over time. At 48 h following CABG, the measured best smoothed sensitivity was 84.8% and the specificity 85.4%. The positive and negative predictive values were 72.7% and 92.8%, respectively, and the overall accuracy was 85.3%. With regard to the prediction accuracy, the risk assessment and prediction of post-operative AF is optimal in the period between 24 and 48 h following CABG.

A Simplified 3D Model of Whole Heart Electrical Activity and 12-Lead ECG Generation

We present a computationally efficient three-dimensional bidomain model of torso-embedded whole heart electrical activity, with spontaneous initiation of activation in the sinoatrial node, incorporating a specialized conduction system with heterogeneous action potential morphologies throughout the heart. The simplified geometry incorporates the whole heart as a volume source, with heart cavities, lungs, and torso as passive volume conductors. We placed four surface electrodes at the limbs of the torso: V R, V L, V F and V GND and six electrodes on the chest to simulate the Einthoven, Goldberger-augmented and precordial leads of a standard 12-lead system. By placing additional seven electrodes at the appropriate torso positions, we were also able to calculate the vectorcardiogram of the Frank lead system. Themodel was able to simulate realistic electrocardiogram (ECG) morphologies for the 12 standard leads, orthogonal X, Y, and Z leads, as well as the vectorcardiogram under normal and pathological heart states. Thus, simplified and easy replicable 3D cardiac bidomain model offers a compromise between computational load and model complexity and can be used as an investigative tool to adjust cell, tissue, and whole heart properties, such as setting ischemic lesions or regions of myocardial infarction, to readily investigate their effects on whole ECG morphology.

Real time P-wave detector based on wavelet analysis

In ECG signals processing it is sometimes necessary to determine the timing of different ECG segments in real time, i.e. in the shortest time after they appeared in the signal. A typical example is P-wave synchronized pacing (heart stimulation synchronized with the P-wave). We used multistage methodology enabled by wavelet transform to delineate the ECG signal and develop a sensitive (98.5%) and reliable P-wave detector.

Continuous multiparameter monitoring of P wave parameters after CABG using wavelet detector

The aim of the study was to develop methodology for long-term study of ECG parameters, in particular the P wave parameters. In this study we address continuous monitoring of different P wave parameters in the group of patients after coronary artery bypass grafting (CABG) in order to examine potential predictors of atrial fibrillation. Lead II of the standard surface ECG was recorded in the period of typically 48 hours in patients after CABG. Dyadic wavelet transform analysis with first derivation of Gaussian smoothing function as a mother wavelet, was used for a QRS and a P wave detection, characterization and delineation. During the recording, for every patient, in every hour, vector of 108 P wave components was calculated, allowing continuous and deeper insight into atrial activity

Simplified 2D Bidomain Model of Whole Heart Electrical Activity and ECG Generation

Abstract The aim of this study was the development of a geometrically simple and highly computationally-efficient two dimensional (2D) biophysical model of whole heart electrical activity, incorporating spontaneous activation of the sinoatrial node (SAN), the specialized conduction system, and realistic surface ECG morphology computed on the torso. The FitzHugh-Nagumo (FHN) equations were incorporated into a bidomain finite element model of cardiac electrical activity, which was comprised of a simplified geometry of the whole heart with the blood cavities, the lungs and the torso as an extracellular volume conductor. To model the ECG, we placed four electrodes on the surface of the torso to simulate three Einthoven leads VI, VII and VIII from the standard 12-lead system. The 2D model was able to reconstruct ECG morphology on the torso from action potentials generated at various regions of the heart, including the sinoatrial node, atria, atrioventricular node, His bundle, bundle branches, Purkinje fibers, and ventricles. Our 2D cardiac model offers a good compromise between computational load and model complexity, and can be used as a first step towards three dimensional (3D) ECG models with more complex, precise and accurate geometry of anatomical structures, to investigate the effect of various cardiac electrophysiological parameters on ECG morphology.

Hilbert-Huang Time-Frequency Analysis of Motor Imagery EEG Data for Brain-Computer Interfaces

Brain-computer interface (BCI) is a technology that provides a non-muscular communication channel between a brain and the outside world. Imagination of left and right hand movements results in spatially distinct brain activation patterns that can be used as control signals for the BCI. Motor imagery (MI) results in the attenuation (event related desynchronization, ERD) or enhancement (event related synchronization, ERS) of amplitude in a certain frequency band of electroencephalogram (EEG). This frequency band can vary between different participants. Therefore time-frequency (TF) analysis is performed in order to extract interesting features from EEG. A simple way of performing TF analysis is by using band power features. In this paper we investigate the perspective of Hilbert-Huang transform (HHT) for extracting TF information used for MI classification. HHT is a method that allows calculation of instantaneous frequency and amplitude of the signal. It does that by decomposing the signal into components for which these parameters can be calculated by means of Hilbert transform. We compare classification accuracy of simple band power features and features obtained by means of HHT on BCI competition IV dataset 2b.

New cardiomechanic pacing lead sensor based on high frequency parameters: experimental studies in sheep

Pacing Lead as a High Frequency Cardiomechanic Sensor. Introduction: The purpose of this study was to investigate the possibility of detecting and quantifying ventricular contraction in sheep utilizing the cardiomechanic sensor based upon the high frequency (HF) parameters measurements on bipolar cardiac pacing leads. Measurement of the HF reflection coefficient yields the lead‐bending signal (LBS) caused by myocardial contraction. The correlation between the lead‐bending acceleration (LBA) expressed as the rate of rise of LBS and LV dP/dt should reveal that LBS may be utilized as a cardiomechanic sensor in implantable cardiac electrotherapy devices.

Influence of active dendrites on firing patterns in a retinal ganglion cell model

Active regional conductances and inhomogeneous distribution of membrane ionic channels in dendrites influence the integration of synaptic inputs in cortical neurons. How these properties shape the response properties of retinal ganglion cells (RGC) in the mammalian retina has remained largely unexplored. In this study, we used a morphologically-realistic RGC computational model to study how active dendritic properties contribute to neural behaviors. Our simulations suggest that the dendritic distribution of voltage-gated ionic channels strongly influences RGC firing patterns, indicating their important contribution to neuronal function.

The Role of Laboratories and Projects in Biomedical Engineering Education

In this paper we present the concept of biomedical instrumentation laboratory exercises and group projects as an example of organisation of practical students’ work. The purpose of the laboratory exercises and projects is to give students the practical knowledge in analysis and design of (in our case) biomedical instrumentation and systems, as well as some biomedical signal processing. The course offers implementation of the concepts and tools that are taught in Biomedical Instrumentation through design examples with real devices, as well as an individual hands-on experience about the concepts that are taught in the course. Our experience showed that it is essential that students are taught of basic principles of the functions of biomedical devices and equipment, but the implementation of group projects gives more motivation and brings to exceptional results.

3D Cardiac Electrical Activity Model

The aim of this study is to develop a new, computationally-efficient, anatomically-realistic 3D bidomain cardiac electrical activity model using widely available software and standard low-cost hardware. The model incorporates whole-heart embedded in a human torso, spontaneous activation of sinoatrial node and specialized conduction system with heterogeneous action potential morphologies. The model is capable of generating realistic body surface electrocardiograms (ECGs) and is proposed as a useful tool for investigating some major issues in heart pathophys-iology and in stimulation; such as simulating and optimizing synchronized electrical cardioversion, defibrillation and pacing stimulation.