Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use.

Influence of Ectopic Beats on Heart Rate Variability Analysis

The analysis of heart rate variability (HRV) plays a dominant role in the study of physiological signal variability. HRV reflects the information of the adjustment of sympathetic and parasympathetic nerves on the cardiovascular system and, thus, is widely used to evaluate the functional status of the cardiovascular system. Ectopic beats may affect the analysis of HRV. However, the quantitative relationship between the burden of ectopic beats and HRV indices, including entropy measures, has not yet been investigated in depth. In this work, we analyzed the effects of different numbers of ectopic beats on several widely accepted HRV parameters in time-domain (SDNN), frequency-domain (LF/HF), as well as non-linear features (SampEn and Pt-SampEn (physical threshold-based SampEn)). The results showed that all four indices were influenced by ectopic beats, and the degree of influence was roughly increased with the increase of the number of ectopic beats. Ectopic beats had the greatest impact on the frequency domain index LF/HF, whereas the Pt-SampEn was minimally accepted by ectopic beats. These results also indicated that, compared with the other three indices, Pt-SampEn had better robustness for ectopic beats.

A FEATURE SELECTION-BASED ALGORITHM FOR DETECTION OF ATRIAL FIBRILLATION USING SHORT-TERM ECG

In the presence of premature atrial contraction (PAC), premature ventricular contraction (PVC) or other ectopic beats, RR intervals (RRIs) may be disturbed, which results in other types of heart disease being misdiagnosed as atrial fibrillation (AF). In this study, a low-complexity AF detection method based on short ECG is proposed, which includes RRIs modification and feature selection. The extracted RRIs are used to determine whether the potential RRI interference exists and to modify it. Next, based on the modified RRIs, the features are evaluated and selected by the methods of correlation criterion, Fisher criterion, and minimum redundancy maximum relevance criterion. Finally, filtered features are classified by the artificial neural network (ANN). The algorithm is validated in a test set including 2332 AF, 313 normal (NOR), 239 atrioventricular block (IAVB), 81 left bundle branch block (LBBB), 624 right bundle branch block (RBBB), 426 PAC and 564 PVC. Compared with the previous detection method of AF based on the RRIs, the proposed method achieved an overall sensitivity of 94.04% and an overall specificity of 86.74%. The specificity of the test set containing only AF and NOR is up to 99.04%. Meanwhile, the overall false-positive rate (FPR) of PAC and PVC can be reduced by 9.19%. While ensuring accuracy, this method effectively reduces the probability of misdiagnosis of PVC and PAC as AF. It is an automatic detection method of AF suitable for inter-patient clinical short-term ECG.

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.

Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

The idea that most physiological systems are complex has become increasingly popular in recent decades [...].