Research

15 pages, 982 KiB

Open AccessArticle

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

by Fernando Pose, Nicolas Ciarrocchi, Carlos Videla and Francisco O. Redelico

Entropy 2023, 25(2), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/e25020267 - 31 Jan 2023

Cited by 2 | Viewed by 1143

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and [...] Read more.

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than

90 %

and

p (s 1) > p (s 720)

. Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than

95 %

, and PE is not sensitive to changes in ICP and

p (s 720) > p (s 1)

. The results show that it could be used for real-time patient monitoring or as input for a machine learning tool. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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16 pages, 330 KiB

Open AccessArticle

Slope Entropy Normalisation by Means of Analytical and Heuristic Reference Values

by David Cuesta-Frau, Mahdy Kouka, Javier Silvestre-Blanes and Víctor Sempere-Payá

Entropy 2023, 25(1), 66; https://0-doi-org.brum.beds.ac.uk/10.3390/e25010066 - 30 Dec 2022

Cited by 1 | Viewed by 1710

Abstract

Slope Entropy (SlpEn) is a very recently proposed entropy calculation method. It is based on the differences between consecutive values in a time series and two new input thresholds to assign a symbol to each resulting difference interval. As the histogram normalisation value, [...] Read more.

Slope Entropy (SlpEn) is a very recently proposed entropy calculation method. It is based on the differences between consecutive values in a time series and two new input thresholds to assign a symbol to each resulting difference interval. As the histogram normalisation value, SlpEn uses the actual number of unique patterns found instead of the theoretically expected value. This maximises the information captured by the method but, as a consequence, SlpEn results do not usually fall within the classical

[0, 1]

interval. Although this interval is not necessary at all for time series classification purposes, it is a convenient and common reference framework when entropy analyses take place. This paper describes a method to keep SlpEn results within this interval, and improves the interpretability and comparability of this measure in a similar way as for other methods. It is based on a max–min normalisation scheme, described in two steps. First, an analytic normalisation is proposed using known but very conservative bounds. Afterwards, these bounds are refined using heuristics about the behaviour of the number of patterns found in deterministic and random time series. The results confirm the suitability of the approach proposed, using a mixture of the two methods. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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11 pages, 1822 KiB

Open AccessArticle

Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain–Computer Interface

by Kun Wang, Feifan Tian, Minpeng Xu, Shanshan Zhang, Lichao Xu and Dong Ming

Entropy 2022, 24(11), 1556; https://0-doi-org.brum.beds.ac.uk/10.3390/e24111556 - 29 Oct 2022

Cited by 6 | Viewed by 1469

Abstract

Motor imagery-based brain–computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level [...] Read more.

Motor imagery-based brain–computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel–Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user’s MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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15 pages, 332 KiB

Open AccessFeature PaperArticle

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures

by Chloe Hinchliffe, Mahinda Yogarajah, Samia Elkommos, Hongying Tang and Daniel Abasolo

Entropy 2022, 24(10), 1348; https://0-doi-org.brum.beds.ac.uk/10.3390/e24101348 - 23 Sep 2022

Cited by 4 | Viewed by 1452

Abstract

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the [...] Read more.

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

22 pages, 725 KiB

Open AccessArticle

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

by Aditya Nalwaya, Kritiprasanna Das and Ram Bilas Pachori

Entropy 2022, 24(10), 1322; https://0-doi-org.brum.beds.ac.uk/10.3390/e24101322 - 20 Sep 2022

Cited by 23 | Viewed by 2033

Abstract

Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for [...] Read more.

Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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12 pages, 3242 KiB

Open AccessArticle

Segmentation Method of Cerebral Aneurysms Based on Entropy Selection Strategy

by Tingting Li, Xingwei An, Yang Di, Jiaqian He, Shuang Liu and Dong Ming

Entropy 2022, 24(8), 1062; https://0-doi-org.brum.beds.ac.uk/10.3390/e24081062 - 01 Aug 2022

Viewed by 1338

Abstract

The segmentation of cerebral aneurysms is a challenging task because of their similar imaging features to blood vessels and the great imbalance between the foreground and background. However, the existing 2D segmentation methods do not make full use of 3D information and ignore [...] Read more.

The segmentation of cerebral aneurysms is a challenging task because of their similar imaging features to blood vessels and the great imbalance between the foreground and background. However, the existing 2D segmentation methods do not make full use of 3D information and ignore the influence of global features. In this study, we propose an automatic solution for the segmentation of cerebral aneurysms. The proposed method relies on the 2D U-Net as the backbone and adds a Transformer block to capture remote information. Additionally, through the new entropy selection strategy, the network pays more attention to the indistinguishable blood vessels and aneurysms, so as to reduce the influence of class imbalance. In order to introduce global features, three continuous patches are taken as inputs, and a segmentation map corresponding to the central patch is generated. In the inference phase, using the proposed recombination strategy, the segmentation map was generated, and we verified the proposed method on the CADA dataset. We achieved a Dice coefficient (DSC) of 0.944, an IOU score of 0.941, recall of 0.946, an F2 score of 0.942, a mAP of 0.896 and a Hausdorff distance of 3.12 mm. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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18 pages, 1282 KiB

Open AccessArticle

Colored Texture Analysis Fuzzy Entropy Methods with a Dermoscopic Application

by Mirvana Hilal, Andreia S. Gaudêncio, Pedro G. Vaz, João Cardoso and Anne Humeau-Heurtier

Entropy 2022, 24(6), 831; https://0-doi-org.brum.beds.ac.uk/10.3390/e24060831 - 15 Jun 2022

Cited by 8 | Viewed by 1828

Abstract

Texture analysis is a subject of intensive focus in research due to its significant role in the field of image processing. However, few studies focus on colored texture analysis and even fewer use information theory concepts. Entropy measures have been proven competent for [...] Read more.

Texture analysis is a subject of intensive focus in research due to its significant role in the field of image processing. However, few studies focus on colored texture analysis and even fewer use information theory concepts. Entropy measures have been proven competent for gray scale images. However, to the best of our knowledge, there are no well-established entropy methods that deal with colored images yet. Therefore, we propose the recent colored bidimensional fuzzy entropy measure,

F u z E n C_{2 D}

, and introduce its new multi-channel approaches,

F u z E n V_{2 D}

and

F u z E n M_{2 D}

, for the analysis of colored images. We investigate their sensitivity to parameters and ability to identify images with different irregularity degrees, and therefore different textures. Moreover, we study their behavior with colored Brodatz images in different color spaces. After verifying the results with test images, we employ the three methods for analyzing dermoscopic images of malignant melanoma and benign melanocytic nevi.

F u z E n C_{2 D}

,

F u z E n V_{2 D}

, and

F u z E n M_{2 D}

illustrate a good differentiation ability between the two—similar in appearance—pigmented skin lesions. The results outperform those of a well-known texture analysis measure. Our work provides the first entropy measure studying colored images using both single and multi-channel approaches. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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16 pages, 5158 KiB

Open AccessArticle

Cardiovascular Signal Entropy Predicts All-Cause Mortality: Evidence from The Irish Longitudinal Study on Ageing (TILDA)

by Silvin P. Knight, Mark Ward, Louise Newman, James Davis, Eoin Duggan, Rose Anne Kenny and Roman Romero-Ortuno

Entropy 2022, 24(5), 676; https://0-doi-org.brum.beds.ac.uk/10.3390/e24050676 - 11 May 2022

Cited by 2 | Viewed by 3127

Abstract

In this study, the relationship between cardiovascular signal entropy and the risk of seven-year all-cause mortality was explored in a large sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that physiological dysregulation might [...] Read more.

In this study, the relationship between cardiovascular signal entropy and the risk of seven-year all-cause mortality was explored in a large sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that physiological dysregulation might be quantifiable by the level of sample entropy (SampEn) in continuously noninvasively measured resting-state systolic (sBP) and diastolic (dBP) blood pressure (BP) data, and that this SampEn measure might be independently predictive of mortality. Participants’ date of death up to 2017 was identified from official death registration data and linked to their TILDA baseline survey and health assessment data (2010). BP was continuously monitored during supine rest at baseline, and SampEn values were calculated for one-minute and five-minute sections of this data. In total, 4543 participants were included (mean (SD) age: 61.9 (8.4) years; 54.1% female), of whom 214 died. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between BP SampEn and all-cause mortality. Results revealed that higher SampEn in BP signals was significantly predictive of mortality risk, with an increase of one standard deviation in sBP SampEn and dBP SampEn corresponding to HRs of 1.19 and 1.17, respectively, in models comprehensively controlled for potential confounders. The quantification of SampEn in short length BP signals could provide a novel and clinically useful predictor of mortality risk in older adults. Full article

(This article belongs to the Special Issue Entropy Algorithms for the Analysis of Biomedical Signals)

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