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Entropy
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Entropy Analysis of ECG and EEG Signals
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Entropy Analysis of ECG and EEG Signals

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1018

Special Issue Editor

Prof. Dr. Marcelo Risk

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Guest Editor
Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB), CONICET-IUHIBA-HIBA, Potosi 4240, C1199, Buenos Aires, Argentina
Interests: biomedical signal processing; ECG analysis; heart rate variability; autonomic nervous system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signals, such as the electrocardiogram (ECG) and electroencephalogram (EEG), are windows to the electrical activities of the heart and brain, both in a noninvasive manner, which reflect the activities and influence of other organs such as the autonomic nervous system, and can be used to study the healthy function as well the state of disease.

ECG and EEG can be analyzed using derived signals such as heart rate variability and the energy of the corresponding frequency bands, respectively.

These signals exhibit nonlinear behaviors, which have been successfully analyzed using entropy-based quantifiers, fractals, and other nonlinear techniques.

For this Special Issue, we welcome original contributions or reviews related to heart rate variability, morphological ECG analysis, the dynamics of EEG sub-band energies, spike timing in EEG, etc., using nonlinear dynamic tools such as entropy and fractality.

Prof. Dr. Marcelo Risk
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • heart rate variability, blood pressure variability, blood volume variability, and other cardiovascular time series
  • energy sub-band decomposition, inter-spike times, and other neurological time series
  • clinical applications of nonlinear times series analysis

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Published Papers (2 papers)

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Research

20 pages, 4005 KB  
Article
EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning
by Hesam Shokouh Alaei, Samaneh Kouchaki, Mahinda Yogarajah and Daniel Abasolo
Entropy 2025, 27(10), 1044; https://doi.org/10.3390/e27101044 - 7 Oct 2025
Abstract
Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. [...] Read more.
Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. Nine entropy measures (Sample, Fuzzy, Permutation, Dispersion, Conditional, Phase, Spectral, Rényi, and Wavelet entropy) were evaluated individually to classify PNES from ES using k-nearest neighbours, Naïve Bayes, linear discriminant analysis, logistic regression, support vector machine, random forest, multilayer perceptron, and XGBoost within a leave-one-subject-out cross-validation framework. In addition, a dynamic state, defined as the entropy difference between interictal and preictal periods, was examined. Sample, Fuzzy, Conditional, and Dispersion entropy were higher in PNES than in ES during interictal recordings (not significant), but significantly lower in the preictal (p < 0.05) and dynamic states (p < 0.01). Spatial mapping and permutation-based importance analyses highlighted O1, O2, T5, F7, and Pz as key discriminative channels. Classification performance peaked in the dynamic state, with Fuzzy entropy and support vector machine achieving the best results (balanced accuracy = 72.4%, F1 score = 77.8%, sensitivity = 74.5%, specificity = 70.4%). These results demonstrate the potential of entropy features for differentiating PNES from ES. Full article
(This article belongs to the Special Issue Entropy Analysis of ECG and EEG Signals)
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24 pages, 1687 KB  
Article
A Novel Co-Designed Multi-Domain Entropy and Its Dynamic Synapse Classification Approach for EEG Seizure Detection
by Guanyuan Feng, Jiawen Li, Yicheng Zhong, Shuang Zhang, Xin Liu, Mang I Vai, Kaihan Lin, Xianxian Zeng, Jun Yuan and Rongjun Chen
Entropy 2025, 27(9), 919; https://doi.org/10.3390/e27090919 - 30 Aug 2025
Viewed by 743
Abstract
Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier [...] Read more.
Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model’s ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection. Full article
(This article belongs to the Special Issue Entropy Analysis of ECG and EEG Signals)
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