Comparative Study of Entropy Sensitivity to Missing Biosignal Data

Abstract

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy-pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.

Entropic Measures of Complexity of Short-Term Dynamics of Nocturnal Heartbeats in an Aging Population

“…Investigations into time intervals between subsequent heartbeats, estimated by the time distance between two subsequent R peaks in a ECG recording, and therefore called RR-intervals, continue to broaden our understanding of the regulation of the cardiovascular (CV) system [1,2]. Entropic measures are assumed to serve as consistent and fair estimates of nonstationary signals and of signals with uncertain values because of inaccuracy or incoherence in recordings [3]. In the following, entropy-based methods are developed and applied to RR-interval signals recorded during the subjects' nocturnal rest to identify changes in the heart rhythm, called RR-increments, caused by healthy aging.…”

Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer’s Disease: Is the Method Superior to Sample Entropy?

“…Since its introduction, FuzzyEn has been used to characterise different types of biomedical signals, such as electromyograms [ 13 , 16 , 17 , 18 ], EEGs [ 19 , 20 ], gait [ 20 ], or heart rate variability [ 20 , 21 ]. Comparative studies with ApEn and SampEn suggest that FuzzyEn outperforms them [ 17 , 19 ].…”

A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement

“…Nonlinear (E1~E9) : low frequency wavelet entropy (E1); high frequency wavelet entropy (E2); normalized low frequency wavelet entropy (E3); normalized high frequency wavelet entropy (E4); ratio of E1 to E2 (E5); total power wavelet entropy (E6) [12]; approximate entropy (E7); sample entropy (E8) [19]; fuzzy entropy (E9) [20]. …”