Parameters Analysis of Sample Entropy, Permutation Entropy and Permutation Ratio Entropy for RR Interval Time Series

Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies.

Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure

Congestive heart failure (CHF) is a chronic cardiovascular condition associated with dysfunction of the autonomic nervous system (ANS). Heart rate variability (HRV) has been widely used to assess ANS. This paper proposes a new HRV analysis method, which uses information-based similarity (IBS) transformation and fuzzy approximate entropy (fApEn) algorithm to obtain the fApEn_IBS index, which is used to observe the complexity of autonomic fluctuations in CHF within 24 h. We used 98 ECG records (54 health records and 44 CHF records) from the PhysioNet database. The fApEn_IBS index was statistically significant between the control and CHF groups (p < 0.001). Compared with the classical indices low-to-high frequency power ratio (LF/HF) and IBS, the fApEn_IBS index further utilizes the changes in the rhythm of heart rate (HR) fluctuations between RR intervals to fully extract relevant information between adjacent time intervals and significantly improves the performance of CHF screening. The CHF classification accuracy of fApEn_IBS was 84.69%, higher than LF/HF (77.55%) and IBS (83.67%). Moreover, the combination of IBS, fApEn_IBS, and LF/HF reached the highest CHF screening accuracy (98.98%) with the random forest (RF) classifier, indicating that the IBS and LF/HF had good complementarity. Therefore, fApEn_IBS effusively reflects the complexity of autonomic nerves in CHF and is a valuable CHF assessment tool.

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.