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.
Highlights
A number of types of entropy estimation measures and their possible applications have been reported in the scientific literature in recent years
The measures of Approximate Entropy (ApEn), Sample Entropy (SampEn), Fuzzy Entropy (FuzzyEn) and Detrended Fluctuation Analysis (DFA) were computed for every record in the experimental set for data loss percentages of 0%, 10%, 30%, 50%, 70%, and 90%
All the experimental records in the dataset underwent different ratios and types of sample loss as described in Section 2, and their entropy metrics were computed in each case
Summary
A number of types of entropy estimation measures and their possible applications have been reported in the scientific literature in recent years. There is a myriad of such applications in the specific biomedical signal framework because biological systems are great entropy generators [4]. An ongoing characterization effort has been lately undertaken to gain a better understanding of signal entropy measures and their properties [4]. Works such as [23,24] have studied the influence of parameters like signal length or thresholds. Garcia-Gonzalez [28] and Molina-Pico [29], assessed robustness against signal outliers
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