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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 17912

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Special Issue Editors

Dr. Francisco Castells

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Guest Editor

Instituto ITACA, Universitat Politècnica de València, Camino de Vera sn, 46022 Valencia, Spain
Interests: biomedical signal processing; biomedical imaging; artificial intelligence; cardiac arrhythmias; cardiomyopathies; acoustics

Prof. Dr. Raquel Cervigón

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Guest Editor

Escuela Politécnica, Universidad de Castilla-La Mancha, Camino del Pozuelo sn, 16071 Cuenca, Spain
Interests: biomedical signal processing; biomedical imaging; cardiac arrhythmias; atrial fibrillation; electrocardiogram; cardiovascular research

Special Issue Information

Dear colleague,

The condition of a physiological system is usually determined by a large number of complex biological phenomena and processes that interact with each other. When this is applied to human beings, some pathologies or malfunctions may depend on reactions that take place at cellular or molecular level. Moreover, the same causing alteration could provoke different clinical outcomes, since age, gender, cardiovascular condition, lifestyle, etc., can either alleviate or aggravate the dysfunction.

The acquisition and analysis of biomedical signals and images are valuable diagnostic tests that are widely employed in medicine for the assessment of the patient’s condition. As a result of physiologic processes, the acquired data may be modulated and affected by the system’s complexity. Accordingly, these data could be regarded as samples of stochastic processes, in occasions difficult to interpret. Still, biomedical data are not completely random, and new methods able to deal with their complexity at different hierarchical levels, time scales and modalities are needed to improve diagnosis as well as the understanding about the interconnections of different physiological systems.

With this in mind, it is not surprising that entropy measurements have been successful in a wide variety of biomedical applications. From Shannon to Sample Entropy, Multiscale or Refined, just to mention a few, different entropy measurements can be defined and adapted to data properties in order to extract meaningful clinical information, which could be overlooked with other approaches.

We encourage researchers to submit contributions employing entropy-based methods in biomedical problems, e.g. aiming to improve clinical diagnosis or current physiological knowledge. Manuscripts reviewing the state-of-the-art of entropy analysis in biomedical applications are also welcome.

Prof. Francisco Castells
Prof. Raquel Cervigón
Guest Editors

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Keywords

Entropy
Complexity
Biomedical Signal Processing
Biomedical Imaging

Published Papers (6 papers)

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Research

10 pages, 313 KiB

Open AccessArticle

Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis

by Borja Vargas, David Cuesta-Frau, Paula González-López, María-José Fernández-Cotarelo, Óscar Vázquez-Gómez, Ana Colás and Manuel Varela

Entropy 2022, 24(4), 510; https://doi.org/10.3390/e24040510 - 5 Apr 2022

Cited by 5 | Viewed by 1638

Abstract

Body temperature is usually employed in clinical practice by strict binary thresholding, aiming to classify patients as having fever or not. In the last years, other approaches based on the continuous analysis of body temperature time series have emerged. These are not only based on absolute thresholds but also on patterns and temporal dynamics of these time series, thus providing promising tools for early diagnosis. The present study applies three time series entropy calculation methods (Slope Entropy, Approximate Entropy, and Sample Entropy) to body temperature records of patients with bacterial infections and other causes of fever in search of possible differences that could be exploited for automatic classification. In the comparative analysis, Slope Entropy proved to be a stable and robust method that could bring higher sensitivity to the realm of entropy tools applied in this context of clinical thermometry. This method was able to find statistically significant differences between the two classes analyzed in all experiments, with sensitivity and specificity above 70% in most cases. Full article

(This article belongs to the Special Issue Entropy in Biomedical Applications)

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9 pages, 496 KiB

Open AccessArticle

Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis

by Jin-Long Chen, Hsuan-Shu Shen, Shih-Yi Peng and Hung-Ming Wang

Entropy 2022, 24(2), 258; https://doi.org/10.3390/e24020258 - 10 Feb 2022

Viewed by 1343

Abstract

Studying heart rate dynamics would help understand the effects caused by a hyperkinetic heart in patients with hyperthyroidism. By using a multiscale entropy (MSE) analysis of heart rate dynamics derived from one-channel electrocardiogram recording, we aimed to compare the system complexity of heart rate dynamics between hyperthyroid patients and control subjects. A decreased MSE complexity index (CI) computed from MSE analysis reflects reduced system complexity. Compared with the control subjects (n = 37), the hyperthyroid patients (n = 37) revealed a significant decrease (p < 0.001) in MSE CI (hyperthyroid patients 10.21 ± 0.37 versus control subjects 14.08 ± 0.21), sample entropy for each scale factor (from 1 to 9), and high frequency power (HF) as well as a significant increase (p < 0.001) in low frequency power (LF) in normalized units (LF%) and ratio of LF to HF (LF/HF). In conclusion, besides cardiac autonomic dysfunction, the system complexity of heart rate dynamics is reduced in hyperthyroidism. This finding implies that the adaptability of the heart rate regulating system is impaired in hyperthyroid patients. Additionally, it might explain the exercise intolerance experienced by hyperthyroid patients. In addition, hyperthyroid patients and control subjects could be distinguished by the MSE CI computed from MSE analysis of heart rate dynamics. Full article

(This article belongs to the Special Issue Entropy in Biomedical Applications)

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11 pages, 2171 KiB

Open AccessArticle

Empirical Mode Decomposition-Derived Entropy Features Are Beneficial to Distinguish Elderly People with a Falling History on a Force Plate Signal

by Li-Wei Chou, Kang-Ming Chang, Yi-Chun Wei and Mei-Kuei Lu

Entropy 2021, 23(4), 472; https://doi.org/10.3390/e23040472 - 16 Apr 2021

Cited by 7 | Viewed by 2256

Abstract

Fall risk prediction is an important issue for the elderly. A center of pressure signal, derived from a force plate, is useful for the estimation of body calibration. However, it is still difficult to distinguish elderly people’s fall history by using a force plate signal. In this study, older adults with and without a history of falls were recruited to stand still for 60 s on a force plate. Forces in the x, y and z directions (Fx, Fy, and Fz) and center of pressure in the anteroposterior (COPx) and mediolateral directions (COPy) were derived. There were 49 subjects in the non-fall group, with an average age of 71.67 (standard derivation: 6.56). There were also 27 subjects in the fall group, with an average age of 70.66 (standard derivation: 6.38). Five signal series—forces in x, y, z (Fx, Fy, Fz), COPX, and COPy directions—were used. These five signals were further decomposed with empirical mode decomposition (EMD) with seven intrinsic mode functions. Time domain features (mean, standard derivation and coefficient of variations) and entropy features (approximate entropy and sample entropy) of the original signals and EMD-derived signals were extracted. Results showed that features extracted from the raw COP data did not differ significantly between the fall and non-fall groups. There were 10 features extracted using EMD, with significant differences observed among fall and non-fall groups. These included four features from COPx and two features from COPy, Fx and Fz. Full article

(This article belongs to the Special Issue Entropy in Biomedical Applications)

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14 pages, 818 KiB

Open AccessArticle

Conditional Entropy: A Potential Digital Marker for Stress

by Soheil Keshmiri

Entropy 2021, 23(3), 286; https://doi.org/10.3390/e23030286 - 26 Feb 2021

Cited by 10 | Viewed by 2608

Abstract

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress. Full article

(This article belongs to the Special Issue Entropy in Biomedical Applications)

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20 pages, 16207 KiB

Open AccessArticle

On Entropy of Probability Integral Transformed Time Series

by Dragana Bajić, Nataša Mišić, Tamara Škorić, Nina Japundžić-Žigon and Miloš Milovanović

Entropy 2020, 22(10), 1146; https://doi.org/10.3390/e22101146 - 12 Oct 2020

Cited by 3 | Viewed by 2714

Abstract

The goal of this paper is to investigate the changes of entropy estimates when the amplitude distribution of the time series is equalized using the probability integral transformation. The data we analyzed were with known properties—pseudo-random signals with known distributions, mutually coupled using statistical or deterministic methods that include generators of statistically dependent distributions, linear and non-linear transforms, and deterministic chaos. The signal pairs were coupled using a correlation coefficient ranging from zero to one. The dependence of the signal samples is achieved by moving average filter and non-linear equations. The applied coupling methods are checked using statistical tests for correlation. The changes in signal regularity are checked by a multifractal spectrum. The probability integral transformation is then applied to cardiovascular time series—systolic blood pressure and pulse interval—acquired from the laboratory animals and represented the results of entropy estimations. We derived an expression for the reference value of entropy in the probability integral transformed signals. We also experimentally evaluated the reliability of entropy estimates concerning the matching probabilities. Full article

(This article belongs to the Special Issue Entropy in Biomedical Applications)

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12 pages, 1784 KiB

Open AccessArticle

Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis

by Jheng-Ru Chen, Yi-Ping Chao, Yu-Wei Tsai, Hsien-Jung Chan, Yung-Liang Wan, Dar-In Tai and Po-Hsiang Tsui

Entropy 2020, 22(9), 1006; https://doi.org/10.3390/e22091006 - 9 Sep 2020

Cited by 22 | Viewed by 6377

Abstract

Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis. Full article

(This article belongs to the Special Issue Entropy in Biomedical Applications)

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