Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

Entropy of Heart Rate on Self-Selected Interval Exercises in Older Women

Non-linear analyzes such as Approximated Entropy (ApEn) and Sample Entropy (SampEn) could show the adaptability of the autonomic nervous system in relation to the dynamic changes caused by exercise. The aims of the study were: a) Investigate the effects of different Self-Selected based Interval Exercises (SSIE) configurations on Heart Rate (HR) entropy; b) Determine whether the stimuli time promote different entropy responses; c) Observe whether exercises with passive self-selected recovery time (SSRT) promote different HR entropy responses compared to those with imposed time and active recovery; and d) Determine whether post-training entropy responses quickly return to baseline. Fifteen older women were randomized to perform six sessions of SSIE and one session of Self-Selected Continuous Exercise (SSCE), with approximately 24 min duration each. The results showed increases on ApEn during the exercises compared to the moments of rest Pre (p < 0.001), Post 6 min (p = 0.003) and Post 12 min (p < 0.001). Results demonstrated that interval exercises (IE) with SSRT, present lower values of ApEn and SampEn regarding the continuous activity (p < 0.05). It was also observed that the entropy values after training returned quickly to levels close to those of pre-exercise rest with a tendency to decrease more pronounced for the continuous. The SSIE were able to promote greater complexity in the HR entropy of older women, allowing greater stabilization of the cardiovascular system, including after training.

Development of a Neurodegenerative Disease Gait Classification Algorithm Using Multiscale Sample Entropy and Machine Learning Classifiers

The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson’s disease (PD), HC vs. Huntington’s disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers.

Electrogram recording and analyzing techniques to optimize selection of target sites for ablation of cardiac arrhythmias

The extracellular electrogram is caused by transmembrane currents that flow into extracellular space during propagation of the electrical impulse. Electrograms are usually recorded in unipolar or bipolar mode that have different characteristics, but provide complementary information. Both recording modes have specific advantages, but also suffer from disadvantages. Techniques to circumvent some of the weaknesses are reviewed. The origin of remote and fractionated deflections and their relation with electrode characteristics are discussed. Epicardial and endocardial sites of origin and breakthrough sites as well as the effect of fatty tissue on extracellular electrograms are presented. Induction of tachycardia to assess the arrhythmogenic area is not always possible because of hemodynamic instability of the patient. Techniques to assess sites with high reentry vulnerability without induction of arrhythmias are outlined such as activation‐repolarization mapping and decremental stimulation. Pitfalls of substrate mapping and techniques to avoid them as omnipolar mapping and characterization of complex electrograms by entropy are presented. Technical aspects that influence electrogram morphology as electrode size, filtering, contact force, and catheter position are delineated. Data from the various publications suggest that a combination of unipolar and bipolar electrogram analysis techniques is helpful to optimize determination of target sites for ablation.

Entropy Mapping Approach for Functional Reentry Detection in Atrial Fibrillation: An In-Silico Study

Catheter ablation of critical electrical propagation sites is a promising tool for reducing the recurrence of atrial fibrillation (AF). The spatial identification of the arrhythmogenic mechanisms sustaining AF requires the evaluation of electrograms (EGMs) recorded over the atrial surface. This work aims to characterize functional reentries using measures of entropy to track and detect a reentry core. To this end, different AF episodes are simulated using a 2D model of atrial tissue. Modified Courtemanche human action potential and Fenton–Karma models are implemented. Action potential propagation is modeled by a fractional diffusion equation, and virtual unipolar EGM are calculated. Episodes with stable and meandering rotors, figure-of-eight reentry, and disorganized propagation with multiple reentries are generated. Shannon entropy (ShEn), approximate entropy (ApEn), and sample entropy (SampEn) are computed from the virtual EGM, and entropy maps are built. Phase singularity maps are implemented as references. The results show that ApEn and SampEn maps are able to detect and track the reentry core of rotors and figure-of-eight reentry, while the ShEn results are not satisfactory. Moreover, ApEn and SampEn consistently highlight a reentry core by high entropy values for all of the studied cases, while the ability of ShEn to characterize the reentry core depends on the propagation dynamics. Such features make the ApEn and SampEn maps attractive tools for the study of AF reentries that persist for a period of time that is similar to the length of the observation window, and reentries could be interpreted as AF-sustaining mechanisms. Further research is needed to determine and fully understand the relation of these entropy measures with fibrillation mechanisms other than reentries.

Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.