# Entropy and Statistical Complexity in Bioelectrical Signals: A Literature Review
## 4 Discussion  
### Application of Entropies in the Analysis of Biomedical Signals  
Theoretically, entropies tend to increase over time. In this context, excessively high entropy values can lead to the destruction or disorganization of the system. In other words, living beings must compensate for the increase in entropy to preserve homeostasis and all associated functions, as they are characterized by a high self-organizational capacity [20]. In the medical field, various entropy measures have been used to analyze a wide range of diseases and pathologies by quantifying the complexity and dynamic behavior of biological and physiological signals (see Table 2). Consequently, different concepts of entropy have been applied to data such as electrocardiography (ECG), electroencephalography (EEG), electrogastrography (EGG), and other biomedical records, which are presented below. Multiple entropy measures have been introduced in recent decades for the study of signals [36]. For example, the electroencephalogram (EEG) [28; 29; 36; 50; 52] has been analyzed using entropic criteria such as Permutation Entropy [28; 50; 52], Sample Entropy [29; 36; 52], Multiscale Entropy (MSE) [36; 50; 52], Approximate Entropy [36; 52], and Tsallis Entropy [36], among others. EEG, combined with the aforementioned entropy measures, has enabled various applications, such as monitoring brain activity [28; 29; 36]; analyzing complexity and uncertainty in biomedical datasets [36]; indicating variations in complexity over different time periods [50]; evaluating signal inconsistency [28; 36]; contrasting the complexity of wakefulness and sleep states (for example, entropy in these signals gradually decreases in healthy adults during sleep) [52]; and analyzing factors associated with Parkinson's disease (PD) in its early stages [36] and Alzheimer's disease (AD) [29; 50]. For the specific case of this research, the study titled \"Analysis of Digestive Disorders from EGG Signals Using Tsallis and Renyi Entropy\" (2016) examined individuals with various digestive disorders, including diarrhea, vomiting, and gastric ulcers. The study evaluated the effectiveness of entropy in analyzing these signals, utilizing different types such as Tsallis and Renyi entropy. It was found that Tsallis entropy was higher in individuals without abnormalities, while Renyi entropy was lower compared to those with abnormalities. These results suggest that both entropic measures can differentiate between normal and pathological states, potentially contributing to the diagnosis of digestive disorders. The analysis applied entropy to assess system complexity in the medical field, effectively distinguishing between normal and anomalous physiological states [72]. Another study provides an innovative perspective on the relationship between electrode size and the quality of EGG signals through the analysis of Renyi entropy. The findings reveal that an increase in the contact surface area of the electrode corresponds to an average increase in the Renyi entropy of normalized EGG signals. This suggests improved signal capture with larger electrodes. This enhancement is particularly pronounced when considering higher values of the parameter \(\alpha\), which regulates the sensitivity of this entropy to the probability range of events. Since Renyi entropy reflects the amount of