A study on feature selection using multi-domain feature extraction for automated k-complex detection.

Background: K-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection. Method: In this paper, we first extract multi-domain features based on time, spectral analysis, and chaotic theory. Those features are extracted from a 0.5-s EEG segment, which is obtained using the sliding window technique. As a result, a vector containing twenty-two features is obtained to represent each segment. Next, we explore several feature selection methods and compare their performance in detecting k-complex. Based on the analysis of the selected features, we identify compact features which are fewer than twenty-two features and deemed as relevant and proceeded to the next step. Additionally, three classical classifiers are employed to evaluate the performance of the feature selection models. Results: The results demonstrate that combining different features significantly improved the k-complex detection performance. The best performance is achieved by applying the feature selection method, which results in an accuracy of 93.03%±7.34, sensitivity of 93.81%±5.62%, and specificity 94.13±5.81, respectively, using a smaller number of the combined feature sets. Conclusion: The proposed method in this study can serve as an efficient tool for the automatic detection of k-complex, which is useful for neurologists or doctors in the diagnosis of sleep research.

The Functional Role of Hyperpolarization Activated Current (I f) on Cardiac Pacemaking in Human vs. in the Rabbit Sinoatrial Node: A Simulation and Theoretical Study.

The cardiac hyperpolarization-activated "funny" current (I f), which contributes to sinoatrial node (SAN) pacemaking, has a more negative half-maximal activation voltage and smaller fully-activated macroscopic conductance in human than in rabbit SAN cells. The consequences of these differences for the relative roles of I f in the two species, and for their responses to the specific bradycardic agent ivabradine at clinical doses have not been systematically explored. This study aims to address these issues, through incorporating rabbit and human I f formulations developed by Fabbri et al. into the Severi et al. model of rabbit SAN cells. A theory was developed to correlate the effect of I f reduction with the total inward depolarising current (I total) during diastolic depolarization. Replacing the rabbit I f formulation with the human one increased the pacemaking cycle length (CL) from 355 to 1,139 ms. With up to 20% I f reduction (a level close to the inhibition of I f by ivabradine at clinical concentrations), a modest increase (~5%) in the pacemaking CL was observed with the rabbit I f formulation; however, the effect was doubled (~12.4%) for the human I f formulation, even though the latter has smaller I f density. When the action of acetylcholine (ACh, 0.1 nM) was considered, a 20% I f reduction markedly increased the pacemaking CL by 37.5% (~27.3% reduction in the pacing rate), which is similar to the ivabradine effect at clinical concentrations. Theoretical analysis showed that the resultant increase of the pacemaking CL is inversely proportional to the magnitude of I total during diastolic depolarization phase: a smaller I f in the model resulted in a smaller I total amplitude, resulting in a slower pacemaking rate; and the same reduction in I f resulted in a more significant change of CL in the cell model with a smaller I total. This explained the mechanism by which a low dose of ivabradine slows pacemaking rate more in humans than in the rabbit. Similar results were seen in the Fabbri et al. model of human SAN cells, suggesting our observations are model-independent. Collectively, the results of study explain why low dose ivabradine at clinically relevant concentrations acts as an effective bradycardic agent in modulating human SAN pacemaking.

Mechanism underlying impaired cardiac pacemaking rhythm during ischemia: A simulation study.

Ischemia in the heart impairs function of the cardiac pacemaker, the sinoatrial node (SAN). However, the ionic mechanisms underlying the ischemia-induced dysfunction of the SAN remain elusive. In order to investigate the ionic mechanisms by which ischemia causes SAN dysfunction, action potential models of rabbit SAN and atrial cells were modified to incorporate extant experimental data of ischemia-induced changes to membrane ion channels and intracellular ion homeostasis. The cell models were incorporated into an anatomically detailed 2D model of the intact SAN-atrium. Using the multi-scale models, the functional impact of ischemia-induced electrical alterations on cardiac pacemaking action potentials (APs) and their conduction was investigated. The effects of vagal tone activity on the regulation of cardiac pacemaker activity in control and ischemic conditions were also investigated. The simulation results showed that at the cellular level ischemia slowed the SAN pacemaking rate, which was mainly attributable to the altered Na+-Ca2+ exchange current and the ATP-sensitive potassium current. In the 2D SAN-atrium tissue model, ischemia slowed down both the pacemaking rate and the conduction velocity of APs into the surrounding atrial tissue. Simulated vagal nerve activity, including the actions of acetylcholine in the model, amplified the effects of ischemia, leading to possible SAN arrest and/or conduction exit block, which are major features of the sick sinus syndrome. In conclusion, this study provides novel insights into understanding the mechanisms by which ischemia alters SAN function, identifying specific conductances as contributors to bradycardia and conduction block.

The virtual heart as a platform for screening drug cardiotoxicity.

To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug-ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug-ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi-physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro-arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro-arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed.