Key result
Cross-sample entropy identifies arrhythmias more consistently than sample entropy for short data lengths.
Why the study?
Accurate detection of arrhythmias such as AF and CHF remains challenging in biomedical signal processing.
p-value: p=<.0001
The CrossSampEn algorithm provides a more consistent nonlinear measure than SampEn for detecting arrhythmias from short and long RR interval series.
CrossSampEn may aid arrhythmia detection from short RR series; leaves open prospective clinical validation.
Background Accurate arrhythmia (atrial fibrillation (AF) and congestive heart failure (CHF)) detection is still a challenge in the biomedical signal‐processing field. Different linear and nonlinear measures of the electrocardiogram (ECG) signal analysis are used to fix this problem. Methods Sample entropy (SampEn) is used as a nonlinear measure based on single series to detect healthy and arrhythmia subjects. To follow this measure, the proposed work presents a nonlinear technique, namely, the cross‐sample entropy (CrossSampEn) based on two series to quantify healthy and arrhythmia subjects. Results The research work consists of 10 records of normal sinus rhythm, 20 records of Fantasia (old group), 10 records of AF, and 10 records of CHF. The method of CrossSampEn has been proposed to obtain the irregularity between two same and different R–R (R peak to peak) interval series of different data lengths. Unlike the SampEn technique, the CrossSampEn technique never awards a ‘not defined’ value for very short data lengths and was found to be more consistent than SampEn. One‐way ANOVA test has validated the proposed algorithm by providing a large F value and p < .0001. The proposed algorithm is also verified by simulated data. Conclusions It is concluded that different RR interval series of approximate 1500 data points and same RR interval series of approximate 1000 data points are required for health‐status detection with embedded dimensions, M = 2 and threshold, r = .2. Also, CrossSampEn has been found more consistent than Sample entropy algorithm.
No takes yet. Share an insight, caveat, or question.
Sharma et al. (2023) studied Arrhythmia (atrial fibrillation and congestive heart failure) (n=50). Cross-sample entropy (CrossSampEn) vs. Sample entropy (SampEn) was evaluated on Detection of healthy and arrhythmia subjects (p=<.0001). The cross-sample entropy algorithm successfully differentiated healthy and arrhythmia subjects and was more consistent than sample entropy for short data lengths (p < .0001).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: