A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings

Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning (rp), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed rp against the traditional threshold (rt). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed rp had better stability than rt, hence more adaptive to detect cardiovascular diseases of CHF and AF.

Section: Methodsmentioning

confidence: 99%

“…Besides detecting CHF subjects, SampEn also applies to atrial fibrillation (AF) detection [ 13 ]. Similar problems appear when recommended threshold is used to discriminate AF subjects.…”

Section: Introductionmentioning

confidence: 99%

A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

Xiong

Liang

Zhu

et al. 2019

“…The aforementioned performance metrics of Se , Sp , and Acc were given at the point of c* . Realizing the importance of the index’s effectiveness for accepting or excluding patients under high probability, we also calculated the performance metrics at the setting of cut-point c for Se > 99% and Sp > 99%, respectively [ 46 ].…”

Section: Data and Experimentsmentioning

confidence: 99%

Improving Accuracy of Heart Failure Detection Using Data Refinement

Xiong

Liang

Zhao

et al. 2020

Self Cite

Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.

“…To solve these problems, a range function was proposed by Omidvarnia et al in a new defined range entropy (RangeEn) [ 23 ]. In a previous study, we combined the concepts of range function and the advantages of COSEn and FuzzyMEn, and thus proposed an entropy-based AF detector, named Entropy AF [ 24 ], which has a better discrimination ability for identifying the AF rhythm from the normal sinus rhythm, for both the MIT-BIH AF database and the clinical wearable AF database.…”

Section: Introductionmentioning

confidence: 99%

Determination of Parameters for an Entropy-Based Atrial Fibrillation Detector

Zhao

Wan

et al. 2021

Self Cite

Entropy algorithm is an important nonlinear method for cardiovascular disease detection due to its power in analyzing short-term time series. In previous a study, we proposed a new entropy-based atrial fibrillation (AF) detector, i.e., EntropyAF, which showed a high classification accuracy in identifying AF and non-AF rhythms. As a variation of entropy measures, EntropyAF has two parameters that need to be initialized before the calculation: (1) tolerance threshold r and (2) similarity weight n. In this study, a comprehensive analysis for the two parameters determination was presented, aiming to achieve a high detection accuracy for AF events. Data were from the MIT-BIH AF database. RR interval recordings were segmented using a 30-beat time window. The parameters r and n were initialized from a relatively small value, then gradually increased, and finally the best parameter combination was determined using grid searching. AUC (area under curve) values from the receiver operator characteristic curve (ROC) were compared under different parameter combinations of parameters r and n, and the results demonstrated that the selection of these two parameters plays an important role in AF/non-AF classification. Small values of parameters r and n can lead to a better detection accuracy than other selections. The best AUC value for AF detection was 98.15%, and the corresponding parameter combinations for EntropyAF were as follows: r = 0.01, n = 0.0625, 0.125, 0.25, or 0.5; r = 0.05 and n = 0.0625, 0.125, or 0.25; and r = 0.10 and n = 0.0625 or 0.125.