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Roediger DJ, Butts J, Falke C, Fiecas MB, Klimes-Dougan B, Mueller BA, Cullen KR. Optimizing the measurement of sample entropy in resting-state fMRI data. Front Neurol 2024; 15:1331365. [PMID: 38426165 PMCID: PMC10902163 DOI: 10.3389/fneur.2024.1331365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. Methods We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. Results When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. Discussion We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.
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Affiliation(s)
- Donovan J. Roediger
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| | - Jessica Butts
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Chloe Falke
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Mark B. Fiecas
- Division of Biostatistics and Health Data Science, School of Public Health, UMN, Minneapolis, MN, United States
| | - Bonnie Klimes-Dougan
- Psychology Department, College of Liberal Arts, UMN, Minneapolis, MN, United States
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
| | - Kathryn R. Cullen
- Department of Psychiatry and Behavioral Sciences, Medical School, University of Minnesota (UMN), Minneapolis, MN, United States
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Babu PRK, Di Martino JM, Chang Z, Perochon S, Carpenter KLH, Compton S, Espinosa S, Dawson G, Sapiro G. Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2023; 14:919-930. [PMID: 37266390 PMCID: PMC10231874 DOI: 10.1109/taffc.2021.3113876] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers' face. Facial landmarks' dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks' dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks' dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD.
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Affiliation(s)
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC. USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
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Babu PRK, Di Martino JM, Chang Z, Perochon S, Aiello R, Carpenter KL, Compton S, Davis N, Franz L, Espinosa S, Flowers J, Dawson G, Sapiro G. Complexity analysis of head movements in autistic toddlers. J Child Psychol Psychiatry 2023; 64:156-166. [PMID: 35965431 PMCID: PMC9771883 DOI: 10.1111/jcpp.13681] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. METHODS Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. RESULTS Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. CONCLUSIONS Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.
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Affiliation(s)
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
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Cuesta-Frau D, Kouka M, Silvestre-Blanes J, Sempere-Payá V. Slope Entropy Normalisation by Means of Analytical and Heuristic Reference Values. ENTROPY (BASEL, SWITZERLAND) 2022; 25:66. [PMID: 36673207 PMCID: PMC9858583 DOI: 10.3390/e25010066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
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, 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.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics (ITI), Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain
| | - Mahdy Kouka
- Department of System Informatics and Computers, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Javier Silvestre-Blanes
- Technological Institute of Informatics (ITI), Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain
| | - Víctor Sempere-Payá
- Technological Institute of Informatics (ITI), Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain
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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records. ENTROPY 2022; 24:e24040533. [PMID: 35455196 PMCID: PMC9030272 DOI: 10.3390/e24040533] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 01/03/2023]
Abstract
Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy.
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Near-Infrared Spectroscopy to Assess Cerebral Autoregulation and Optimal Mean Arterial Pressure in Patients With Hypoxic-Ischemic Brain Injury: A Prospective Multicenter Feasibility Study. Crit Care Explor 2020; 2:e0217. [PMID: 33063026 PMCID: PMC7523861 DOI: 10.1097/cce.0000000000000217] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Supplemental Digital Content is available in the text. We provide preliminary multicenter data to suggest that recruitment and collection of physiologic data necessary to quantify cerebral autoregulation and individualized blood pressure targets are feasible in postcardiac arrest patients. We evaluated the feasibility of a multicenter protocol to enroll patients across centers, as well as collect continuous recording (≥ 80% of monitoring time) of regional cerebral oxygenation and mean arterial pressure, which is required to quantify cerebral autoregulation, using the cerebral oximetry index, and individualized optimal mean arterial pressure thresholds. Additionally, we conducted an exploratory analysis to assess if an increased percentage of monitoring time where mean arterial pressure was greater than or equal to 5 mm Hg below optimal mean arterial pressure, percentage of monitoring time with dysfunctional cerebral autoregulation (i.e., cerebral oximetry index ≥ 0.3), and time to return of spontaneous circulation were associated with an unfavorable neurologic outcome (i.e., 6-mo Cerebral Performance Category score ≥ 3).
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Application of the Variance Delay Fuzzy Approximate Entropy for Autonomic Nervous System Fluctuation Analysis in Obstructive Sleep Apnea Patients. ENTROPY 2020; 22:e22090915. [PMID: 33286684 PMCID: PMC7597154 DOI: 10.3390/e22090915] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 07/21/2020] [Accepted: 07/27/2020] [Indexed: 12/12/2022]
Abstract
Obstructive sleep apnea (OSA) is a fatal respiratory disease occurring in sleep. OSA can induce declined heart rate variability (HRV) and was reported to have autonomic nerve system (ANS) dysfunction. Variance delay fuzzy approximate entropy (VD_fApEn) was proposed as a nonlinear index to study the fluctuation change of ANS in OSA patients. Sixty electrocardiogram (ECG) recordings of the PhysioNet database (20 normal, 14 mild-moderate OSA, and 26 severe OSA) were intercepted for 6 h and divided into 5-min segments. HRV analysis were adopted in traditional frequency domain, and nonlinear HRV indices were also calculated. Among these indices, VD_fApEn could significantly differentiate among the three groups (p < 0.05) compared with the ratio of low frequency power and high frequency power (LF/HF ratio) and fuzzy approximate entropy (fApEn). Moreover, the VD_fApEn (90%) reached a higher OSA screening accuracy compared with LF/HF ratio (80%) and fApEn (78.3%). Therefore, VD_fApEn provides a potential clinical method for ANS fluctuation analysis in OSA patients and OSA severity analysis.
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Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring. ENTROPY 2020; 22:e22030319. [PMID: 33286093 PMCID: PMC7516770 DOI: 10.3390/e22030319] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/14/2022]
Abstract
Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.
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Dong X, Chen C, Geng Q, Cao Z, Chen X, Lin J, Jin Y, Zhang Z, Shi Y, Zhang XD. An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals. ENTROPY 2019; 21:e21030274. [PMID: 33266989 PMCID: PMC7514754 DOI: 10.3390/e21030274] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/08/2019] [Accepted: 03/09/2019] [Indexed: 11/17/2022]
Abstract
Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.
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Affiliation(s)
- Xinzheng Dong
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China;
- Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China
| | - Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
| | - Qingshan Geng
- Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou 510080, China;
| | - Zhixin Cao
- Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China; (Z.C.); (Y.S.)
| | - Xiaoyan Chen
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; (X.C.); (J.L.)
| | - Jinxiang Lin
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; (X.C.); (J.L.)
| | - Yu Jin
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
| | - Zhaozhi Zhang
- School of Law, Washington University, St. Louis, MO 63130, USA;
| | - Yan Shi
- Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China; (Z.C.); (Y.S.)
- Department of Mechanical and Electronic Engineering, Beihang University, Beijing 100191, China
| | - Xiaohua Douglas Zhang
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
- Correspondence: ; Tel: +853-8822-4813
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Li Y, Pan W, Li K, Jiang Q, Liu G. Sliding Trend Fuzzy Approximate Entropy as a Novel Descriptor of Heart Rate Variability in Obstructive Sleep Apnea. IEEE J Biomed Health Inform 2019; 23:175-183. [DOI: 10.1109/jbhi.2018.2790968] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Raghu S, Sriraam N, Kumar GP, Hegde AS. A Novel Approach for Real-Time Recognition of Epileptic Seizures Using Minimum Variance Modified Fuzzy Entropy. IEEE Trans Biomed Eng 2018; 65:2612-2621. [PMID: 29993510 DOI: 10.1109/tbme.2018.2810942] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Validation of epileptic seizures annotations from long-term electroencephalogram (EEG) recordings is a tough and tedious task for the neurological community. It is a well-known fact that computerized qualitative methods thoroughly assess the complex brain dynamics toward seizure detection and proven as one of the acceptable clinical indicators. METHODS This research study suggests a novel approach for real-time recognition of epileptic seizure from EEG recordings by a technique referred as minimum variance modified fuzzy entropy (MVMFzEn). Multichannel EEG recordings of 4.36 h of epileptic seizures and 25.74 h of normal EEG were considered. Signal processing techniques such as filters and independent component analysis were appropriated to reduce noise and artifacts. Unlike, the predefined fuzzy membership function, the modified fuzzy entropy utilizes relative energy as a membership function followed by scaling operation to obtain the feature. RESULTS Results revealed that MVMFzEn drops abruptly during an epileptic activity and this fact was used to set a threshold. An automated threshold derived from MVMFzEn assesses the classification efficiency of the given data during validation. It was observed from the results that the proposed method yields a classification accuracy of 100% without the use of any classifier. CONCLUSION The graphical user interface was designed in MATLAB to automatically label the normal and epileptic segments in the long-term EEG recordings. SIGNIFICANCE The ground truth clinical validation using validation specificity and validation sensitivity confirms the suitability of the proposed technique for automated annotation of epileptic seizures in real time.
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Simons S, Espino P, Abásolo D. Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer's Disease: Is the Method Superior to Sample Entropy? ENTROPY 2018; 20:e20010021. [PMID: 33265112 PMCID: PMC7512198 DOI: 10.3390/e20010021] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 12/20/2017] [Accepted: 12/28/2017] [Indexed: 12/13/2022]
Abstract
Alzheimer’s disease (AD) is the most prevalent form of dementia in the world, which is characterised by the loss of neurones and the build-up of plaques in the brain, causing progressive symptoms of memory loss and confusion. Although definite diagnosis is only possible by necropsy, differential diagnosis with other types of dementia is still needed. An electroencephalogram (EEG) is a cheap, portable, non-invasive method to record brain signals. Previous studies with non-linear signal processing methods have shown changes in the EEG due to AD, which is characterised reduced complexity and increased regularity. EEGs from 11 AD patients and 11 age-matched control subjects were analysed with Fuzzy Entropy (FuzzyEn), a non-linear method that was introduced as an improvement over the frequently used Approximate Entropy (ApEn) and Sample Entropy (SampEn) algorithms. AD patients had significantly lower FuzzyEn values than control subjects (p < 0.01) at electrodes T6, P3, P4, O1, and O2. Furthermore, when diagnostic accuracy was calculated using Receiver Operating Characteristic (ROC) curves, FuzzyEn outperformed both ApEn and SampEn, reaching a maximum accuracy of 86.36%. These results suggest that FuzzyEn could increase the insight into brain dysfunction in AD, providing potentially useful diagnostic information. However, results depend heavily on the input parameters that are used to compute FuzzyEn.
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Affiliation(s)
- Samantha Simons
- Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Pedro Espino
- Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
| | - Daniel Abásolo
- Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
- Correspondence: ; Tel.: +44-(0)1483-682971
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13
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Application of Sample Entropy Based LMD-TFPF De-Noising Algorithm for the Gear Transmission System. ENTROPY 2016. [DOI: 10.3390/e18110414] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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14
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Chen W, Zheng L, Li K, Wang Q, Liu G, Jiang Q. A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement. PLoS One 2016; 11:e0165304. [PMID: 27835634 PMCID: PMC5105944 DOI: 10.1371/journal.pone.0165304] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 10/10/2016] [Indexed: 01/01/2023] Open
Abstract
Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree-based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.
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Affiliation(s)
- Wenhui Chen
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Lianrong Zheng
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Kunyang Li
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Qian Wang
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Guanzheng Liu
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
| | - Qing Jiang
- School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Science and Technology Planning Project of Guangdong Province, Guangzhou, Guangdong, China.,Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device, Guangzhou, Guangdong, China
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15
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Pu J, Xu H, Wang Y, Cui H, Hu Y. Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study. Cogn Neurodyn 2016; 10:367-73. [PMID: 27668016 PMCID: PMC5018015 DOI: 10.1007/s11571-016-9394-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 06/09/2016] [Accepted: 06/23/2016] [Indexed: 11/26/2022] Open
Abstract
Spinal cord injury (SCI) is a high-cost disability and may cause permanent loss of movement and sensation below the injury location. The chance of cure in human after SCI is extremely limited. Instead, neural regeneration could have been seen in animals after SCI, and such regeneration could be retarded by blocking neural plasticity pathways, showing the importance of neural plasticity in functional recovery. As an indicator of nonlinear dynamics in the brain, sample entropy was used here in combination with detrended fluctuation analysis (DFA) and Kolmogorov complexity to quantify functional plasticity changes in spontaneous EEG recordings of rats before and after SCI. The results showed that the sample entropy values were decreased at the first day following injury then gradually increased during recovery. DFA and Kolmogorov complexity results were in consistent with sample entropy, showing the complexity of the EEG time series was lost after injury and partially regained in 1 week. The tendency to regain complexity is in line with the observation of behavioral rehabilitation. A critical time point was found during the recovery process after SCI. Our preliminary results suggested that the combined use of these nonlinear dynamical metrics could provide a quantitative and predictive way to assess the change of neural plasticity in a spinal cord injury rat model.
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Affiliation(s)
- Jiangbo Pu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
| | - Hanhui Xu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
| | - Yazhou Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China
| | - Hongyan Cui
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
| | - Yong Hu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China
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16
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Entropy and Recurrence Measures of a Financial Dynamic System by an Interacting Voter System. ENTROPY 2015. [DOI: 10.3390/e17052590] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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17
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Entropic Measures of Complexity of Short-Term Dynamics of Nocturnal Heartbeats in an Aging Population. ENTROPY 2015. [DOI: 10.3390/e17031253] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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