Cardiac signals and the interference of reward on attention and inhibitory control

Interoceptive responses can act as potent cues to cognition and behavior; discrete cardiac signals can shape emotional and motivational adaptation towards reward-related cues, but also affect response inhibition. Novel addiction perspectives posit an interoceptive basis for the interplay between reward processing and inhibitory control, but there is a lack of behavioral evidence for this relationship. In this registered report we extend on previous findings to examine how reward cues interact with cardiac-facilitated attention and motor inhibition. Across two sessions, a sample of 35 social drinkers will complete a visual search task (VST) and two instances of a stop signal task (SST). In each task, alcohol or neutral cues will be presented as targets or distractors respectively. In the VST, target stimuli will be presented synchronized with participants’ cardiac phase (systole vs. diastole), examining how cardiac signals support alcohol attentional biases. In a modified SST, Go cues will appear synchronized with cardiac phase while alcohol or neutral cues appear as distractors, examining how cardiac signals increase reward interference in inhibitory control. Finally, in another instance of the SST, Stop signals will appear synchronized with cardiac phase, examining whether interoceptive signals can improve inhibitory control in the presence of reward cues. We hypothesize, at systole, higher attentional biases and interference in inhibitory control for alcohol cues, and that Stop signals can facilitate response inhibition. These results can provide evidence for the role of cardiac signaling in alcohol attentional biases and inhibitory control, extending our understanding of the interoceptive components of addiction.

Parallel Distributed Framework for State Space Adaptive Filter for Removal of PLI from Cardiac Signals

Cardiac signals are often corrupted by artefacts like power line interference (PLI) which may mislead the cardiologists to correctly diagnose the critical cardiac diseases. The cardiac signals like high resolution electrocardiogram (HRECG), ultra-high frequency ECG (UHF-ECG) and intracardiac electrograms are the specialized techniques in which higher frequency component of interest up to 1 KHz are observed. Therefore, a state space recursive least square (SSRLS) adaptive algorithm is applied for the removal of PLI and its harmonics. The SSRLS algorithm is an effective approach which extracts the desired cardiac signals from the observed signal without any need of reference signal. However, SSRLS is inherited computational heavy algorithm; therefore, filtration of increased number of PLI harmonics bestow an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed SSRLS (PD-SSRLS) algorithm is introduced which runs the computationally expensive SSRLS adaptive algorithm parallely. The proposed architecture efficiently removes the PLI along with its harmonics even the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSRLS scheme provides less computational cost as compared to sequentially operated SSRLS algorithm. A comparison has been drawn between the proposed PD-SSRLS algorithm and sequentially operated SSRLS algorithm in term of qualitative and quantitative performances. The simulation results show that the proposed PD-SSRLS architecture provides almost same qualitative and quantitative performances than that of sequentially operated SSRLS algorithm with less computational cost.

Development of an Algorithm for Clustering Cardiac ECG Signals with Post-Correction for Long-Term ECG Monitoring

Introduction. The most common method for diagnosing cardiovascular diseases is the method of ECG monitoring. In order to facilitate the analysis of the obtained monitorograms, special software solutions for automated ECG processing are required. One possible approach is the use of algorithms for automated ECG processing. Such algorithms perform clustering of cardiac signals by dividing the ECG into complexes of similar cardiac signals. The most representative complexes obtained by statistical averaging are subject to further analysis.Aim. Development of an algorithm for automated ECG processing, which performs clustering of cardiac signals by dividing the ECG into complexes of similar cardiac signals.Materials and methods. Experimental testing of the developed software was carried out using patient records provided by the Pavlov First State Medical University of St Petersburg. The software module was implemented in the MatLab environment.Results. An algorithm for clustering cardiac signals with post-correction for the tasks of long-term ECG monitoring and a software module on its basis were proposed.Conclusion. The presence of a small number of reference cardiac signal complexes, obtained through ECG processing using the proposed algorithm, allows physicians to optimize the process of ECG analysis. The as- obtained information serves as a basis for assessing dynamic changes in the shape and other parameters of cardiac signals for both a particular patient and groups of patients. The paper considers the effect of synchronization errors of clustered cardiac signals on the shape of the averaged cardiac complex. The classical solution to the deconvolution problem leads to significant errors in finding an estimate of the true form of a cardiac signal complex. On the basis of analytical calculations, expressions were obtained for the correction of clustered cardiac signals. Such correction was shown to reduce clusterization errors associated with desynchronization, which creates a basis for investigating the fine structure of ECG signals.

Multi-Chaotic Analysis of Inter-Beat (R-R) Intervals in Cardiac Signals for Discrimination between Normal and Pathological Classes

Cardiac signals have complex structures representing a combination of simpler structures. In this paper, we develop a new data analytic tool that can extract the complex structures of cardiac signals using the framework of multi-chaotic analysis, which is based on the p-norm for calculating the largest Lyapunov exponent (LLE). Appling the p-norm is useful for deriving the spectrum of the generalized largest Lyapunov exponents (GLLE), which is characterized by the width of the spectrum (which we denote by W). This quantity measures the degree of multi-chaos of the process and can potentially be used to discriminate between different classes of cardiac signals. We propose the joint use of the GLLE and spectrum width to investigate the multi-chaotic behavior of inter-beat (R-R) intervals of cardiac signals recorded from 54 healthy subjects (hs), 44 subjects diagnosed with congestive heart failure (chf), and 25 subjects diagnosed with atrial fibrillation (af). With the proposed approach, we build a regression model for the diagnosis of pathology. Multi-chaotic analysis showed a good performance, allowing the underlying dynamics of the system that generates the heart beat to be examined and expert systems to be built for the diagnosis of cardiac pathologies.