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Shimura M, Mizumoto A, Xia Y, Shimomura Y. Multipoint surface electromyography measurement using bull's-eye electrodes for wide-area topographic analysis. J Physiol Anthropol 2023; 42:24. [PMID: 37891686 PMCID: PMC10612298 DOI: 10.1186/s40101-023-00342-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Surface electromyography (sEMG) is primarily used to analyze individual and neighboring muscle activity. However, using a broader approach can enable simultaneous measurement of multiple muscles, which is essential for understanding muscular coordination. Using the "bull's-eye electrode," which allows bipolar derivation without directional dependence, enables wide-area multipoint sEMG measurements. This study aims to establish a multipoint measurement system and demonstrate its effectiveness and evaluates forearm fatigue and created topographic maps during a grasping task. METHODS Nine healthy adults with no recent arm injuries or illnesses participated in this study. They performed grasping tasks using their dominant hand, while bull's-eye electrodes recorded their muscle activity. To validate the effectiveness of the system, we calculated the root mean squares of muscle activity and entropy, an indicator of muscle activity distribution, and compared them over time. RESULTS The entropy analysis demonstrated a significant time-course effect with increased entropy over time, suggesting increased forearm muscle uniformity, which is possibly indicative of fatigue. Topographic maps visually displayed muscle activity, revealing notable intersubject variations. DISCUSSION Bull's-eye electrodes facilitated the capture of nine homogeneous muscle activity points, enabling the creation of topographic images. The entropy increased progressively, suggesting an adaptive muscle coordination response to fatigue. Despite some limitations, such as inadequate measurement of the forearm muscles' belly, the system is an unconventional measurement method. CONCLUSION This study established a robust system for wide-area multipoint sEMG measurements using a bull's-eye electrode setup. This system effectively evaluates muscle fatigue and provides a comprehensive topographic view of muscle activity. These results mark a significant step towards developing a future multichannel sEMG system with enhanced measurement points and improved wearability. TRIAL REGISTRATION This study was approved by the Ethics Committee of Chiba University Graduate School of Engineering (acceptance number: R4-12, Acceptance date: November 04, 2022).
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Affiliation(s)
- Megumi Shimura
- Graduate School of Science and Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba City, 2638522, Japan.
| | - Akihiko Mizumoto
- Graduate School of Science and Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba City, 2638522, Japan
| | - Yali Xia
- Design Research Institute, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba City, 2638522, Japan
| | - Yoshihiro Shimomura
- Design Research Institute, Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba City, 2638522, Japan
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2
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Shivaraja TR, Remli R, Kamal N, Wan Zaidi WA, Chellappan K. Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:3654. [PMID: 37050713 PMCID: PMC10098757 DOI: 10.3390/s23073654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.
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Affiliation(s)
- Theeban Raj Shivaraja
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Rabani Remli
- Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
| | - Noorfazila Kamal
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Wan Asyraf Wan Zaidi
- Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
| | - Kalaivani Chellappan
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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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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4
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Kouka M, Cuesta-Frau D. Slope Entropy Characterisation: The Role of the δ Parameter. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1456. [PMID: 37420476 DOI: 10.3390/e24101456] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/16/2022] [Accepted: 09/21/2022] [Indexed: 07/09/2023]
Abstract
Many time series entropy calculation methods have been proposed in the last few years. They are mainly used as numerical features for signal classification in any scientific field where data series are involved. We recently proposed a new method, Slope Entropy (SlpEn), based on the relative frequency of differences between consecutive samples of a time series, thresholded using two input parameters, γ and δ. In principle, δ was proposed to account for differences in the vicinity of the 0 region (namely, ties) and, therefore, was usually set at small values such as 0.001. However, there is no study that really quantifies the role of this parameter using this default or other configurations, despite the good SlpEn results so far. The present paper addresses this issue, removing δ from the SlpEn calculation to assess its real influence on classification performance, or optimising its value by means of a grid search in order to find out if other values beyond the 0.001 value provide significant time series classification accuracy gains. Although the inclusion of this parameter does improve classification accuracy according to experimental results, gains of 5% at most probably do not support the additional effort required. Therefore, SlpEn simplification could be seen as a real alternative.
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Affiliation(s)
- Mahdy Kouka
- Department of System Informatics and Computers, Universitat Politècnica de València, 03801 Alcoy, Spain
| | - David Cuesta-Frau
- Technological Institute of Informatics, Universitat Politècnica de València, 03801 Alcoy, Spain
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Hussain L, Malibari AA, Alzahrani JS, Alamgeer M, Obayya M, Al-Wesabi FN, Mohsen H, Hamza MA. Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI. Sci Rep 2022; 12:15389. [PMID: 36100621 PMCID: PMC9470580 DOI: 10.1038/s41598-022-19563-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractAccurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.
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Wang Y, Li J, Zeng L, Wang H, Yang T, Shao Y, Weng X. Open Eyes Increase Neural Oscillation and Enhance Effective Brain Connectivity of the Default Mode Network: Resting-State Electroencephalogram Research. Front Neurosci 2022; 16:861247. [PMID: 35573310 PMCID: PMC9092973 DOI: 10.3389/fnins.2022.861247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
The default mode network (DMN) has a unique activity pattern in the resting brain. Studies on resting-state brain activity are helpful to identify various brain dynamic characteristics of patients with mental diseases and those of healthy people. The brain produces a series of changes in different eye states. However, the relationship between eye states and the DMN, which is closely related to the resting state, has not been widely examined. This study recruited 42 healthy students aged 17–22. Participants completed the Profile of Mood States questionnaire. Thereafter, the electroencephalogram data was collected with the patients’ eyes open and closed. Changes in neural oscillation and the DMN’s information transmission during different eye openness states were compared. The results showed that the neural oscillation activities of the parietal-occipital network such as the superior parietal lobule and precuneus were significantly enhanced in the eyes open state. In addition, the effective connectivity within the DMN was enhanced during opened eyes, especially from the left precuneus to the left posterior cingulate cortex, and this connectivity was negatively correlated with the Vigor-Activity mood state in the eyes open state. The activity of the DMN in the resting-state is regulated by eye states, which may relate to mood and emotional perception.
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Affiliation(s)
- Yi Wang
- Department of Physical Education, Renmin University of China, Beijing, China.,School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Jialu Li
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Lingjing Zeng
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Haiteng Wang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Tianyi Yang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing, China
| | - Xiechuan Weng
- Beijing Institute of Basic Medical Sciences, Beijing, China
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West C, Woldman W, Oak K, McLean B, Shankar R. A Review of Network and Computer Analysis of Epileptiform Discharge Free EEG to Characterize and Detect Epilepsy. Clin EEG Neurosci 2022; 53:74-78. [PMID: 33881950 DOI: 10.1177/15500594211008285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objectives. There is emerging evidence that network/computer analysis of epileptiform discharge free electroencephalograms (EEGs) can be used to detect epilepsy, improve diagnosis and resource use. Such methods are automated and can be performed on shorter recordings of EEG. We assess the evidence and its strength in the area of seizure detection from network/computer analysis of epileptiform discharge free EEG. Methods. A scoping review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance was conducted with a literature search of Embase, Medline and PsychINFO. Predesigned inclusion/exclusion criteria were applied to selected articles. Results. The initial search found 3398 articles. After duplicate removal and screening, 591 abstracts were reviewed, 64 articles were selected and read leading to 20 articles meeting the requisite inclusion/exclusion criteria. These were 9 reports and 2 cross-sectional studies using network analysis to compare and/or classify EEG. One review of 17 reports and 10 cross-sectional studies only aimed to classify the EEGs. One cross-sectional study discussed EEG abnormalities associated with autism. Conclusions. Epileptiform discharge free EEG features derived from network/computer analysis differ significantly between people with and without epilepsy. Diagnostic algorithms report high accuracies and could be clinically useful. There is a lack of such research within the intellectual disability (ID) and/or autism populations, where epilepsy is more prevalent and there are additional diagnostic challenges.
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Affiliation(s)
- Caitlin West
- 171002Exeter Medical School, Knowledge Spa, Truro, UK
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, 1724University of Birmingham, Birmingham, UK
| | - Katy Oak
- 8028Royal Cornwall Hospitals Trust Truro, Truro, UK
| | | | - Rohit Shankar
- 7491Cornwall Partnership NHS Foundation Trust, Truro, UK.,Cornwall Intellectual Disability Equitable Research (CIDER), University of Plymouth Medical School, Truro, UK
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8
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Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions. Symmetry (Basel) 2021. [DOI: 10.3390/sym13112178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.
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Matilla-García M, Morales I, Rodríguez JM, Ruiz Marín M. Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction via Symbolic Dynamics. ENTROPY 2021; 23:e23020221. [PMID: 33670103 PMCID: PMC7916852 DOI: 10.3390/e23020221] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/04/2021] [Accepted: 02/08/2021] [Indexed: 12/03/2022]
Abstract
The modeling and prediction of chaotic time series require proper reconstruction of the state space from the available data in order to successfully estimate invariant properties of the embedded attractor. Thus, one must choose appropriate time delay τ∗ and embedding dimension p for phase space reconstruction. The value of τ∗ can be estimated from the Mutual Information, but this method is rather cumbersome computationally. Additionally, some researchers have recommended that τ∗ should be chosen to be dependent on the embedding dimension p by means of an appropriate value for the time delay τw=(p−1)τ∗, which is the optimal time delay for independence of the time series. The C-C method, based on Correlation Integral, is a method simpler than Mutual Information and has been proposed to select optimally τw and τ∗. In this paper, we suggest a simple method for estimating τ∗ and τw based on symbolic analysis and symbolic entropy. As in the C-C method, τ∗ is estimated as the first local optimal time delay and τw as the time delay for independence of the time series. The method is applied to several chaotic time series that are the base of comparison for several techniques. The numerical simulations for these systems verify that the proposed symbolic-based method is useful for practitioners and, according to the studied models, has a better performance than the C-C method for the choice of the time delay and embedding dimension. In addition, the method is applied to EEG data in order to study and compare some dynamic characteristics of brain activity under epileptic episodes
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Affiliation(s)
- Mariano Matilla-García
- Facultad de Economicas y Empresariales, Universidad Nacional de Educación a Distancia (UNED), 28050 Madrid, Spain
- Correspondence:
| | | | - Jose Miguel Rodríguez
- Departamento Metodos Cuantitativos, Ciencias Juridicas y Lenguas Modernas, Universidad Politecnica de Cartagena, 30201 Cartagena, Spain; (J.M.R.); (M.R.M.)
| | - Manuel Ruiz Marín
- Departamento Metodos Cuantitativos, Ciencias Juridicas y Lenguas Modernas, Universidad Politecnica de Cartagena, 30201 Cartagena, Spain; (J.M.R.); (M.R.M.)
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Perceived Loudness Sensitivity Influenced by Brightness in Urban Forests: A Comparison When Eyes Were Opened and Closed. FORESTS 2020. [DOI: 10.3390/f11121242] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Soundscape plays a positive, health-related role in urban forests, and there is a competitive allocation of cognitive resources between soundscapes and lightscapes. This study aimed to explore the relationship between perceived loudness sensitivity and brightness in urban forests through eye opening and closure. Questionnaires and measuring equipment were used to gather soundscape and lightscape information at 44 observation sites in urban forested areas. Diurnal variations, Pearson’s correlations, and formula derivations were then used to analyze the relationship between perception sensitivity and how perceived loudness sensitivity was influenced by lightscape. Our results suggested that soundscape variation plays a role in audio–visual perception in urban forests. Our findings also showed a gap in perception sensitivity between loudness and brightness, which conducted two opposite conditions bounded by 1.24 dBA. Furthermore, we found that the effect of brightness on perceived loudness sensitivity was limited if variations of brightness were sequential and weak. This can facilitate the understanding of individual perception to soundscape and lightscape in urban forests when proposing suitable design plans.
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Hussain L, Aziz W, Khan IR, Alkinani MH, Alowibdi JS. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 18:69-91. [PMID: 33525081 DOI: 10.3934/mbe.2021004] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, 13100, Muzaffarabad, Pakistan
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, 13230, Muzaffarabad, Pakistan
| | - Wajid Aziz
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Ishtiaq Rasool Khan
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Monagi H Alkinani
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
| | - Jalal S Alowibdi
- Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
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Hussain L, Saeed S, Awan IA, Idris A, Nadeem MSA, Chaudhry QUA. Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies. Curr Med Imaging 2020; 15:595-606. [PMID: 32008569 DOI: 10.2174/1573405614666180718123533] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 05/26/2018] [Accepted: 07/10/2018] [Indexed: 02/08/2023]
Abstract
BACKGROUND Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner. OBJECTIVE The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques. METHODS In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset. RESULTS The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98). CONCLUSION The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.
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Affiliation(s)
- Lal Hussain
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Sharjil Saeed
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Adnan Idris
- Department of Computer Sciences & Information Technology, University of Poonch Rawalakot, Rawalakot, Pakistan
| | - Malik Sajjad Ahmed Nadeem
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Qurat-Ul-Ain Chaudhry
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
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13
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Abbasi AA, Hussain L, Awan IA, Abbasi I, Majid A, Nadeem MSA, Chaudhary QA. Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn Neurodyn 2020; 14:523-533. [PMID: 32655715 PMCID: PMC7334337 DOI: 10.1007/s11571-020-09587-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 01/24/2020] [Accepted: 03/27/2020] [Indexed: 01/20/2023] Open
Abstract
Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.
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Affiliation(s)
- Adeel Ahmed Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Lal Hussain
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Imran Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Abdul Majid
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Malik Sajjad Ahmed Nadeem
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Quratul-Ain Chaudhary
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
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Using the Information Provided by Forbidden Ordinal Patterns in Permutation Entropy to Reinforce Time Series Discrimination Capabilities. ENTROPY 2020; 22:e22050494. [PMID: 33286267 PMCID: PMC7516977 DOI: 10.3390/e22050494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/06/2020] [Accepted: 04/20/2020] [Indexed: 11/17/2022]
Abstract
Despite its widely tested and proven usefulness, there is still room for improvement in the basic permutation entropy (PE) algorithm, as several subsequent studies have demonstrated in recent years. Some of these new methods try to address the well-known PE weaknesses, such as its focus only on ordinal and not on amplitude information, and the possible detrimental impact of equal values found in subsequences. Other new methods address less specific weaknesses, such as the PE results' dependence on input parameter values, a common problem found in many entropy calculation methods. The lack of discriminating power among classes in some cases is also a generic problem when entropy measures are used for data series classification. This last problem is the one specifically addressed in the present study. Toward that purpose, the classification performance of the standard PE method was first assessed by conducting several time series classification tests over a varied and diverse set of data. Then, this performance was reassessed using a new Shannon Entropy normalisation scheme proposed in this paper: divide the relative frequencies in PE by the number of different ordinal patterns actually found in the time series, instead of by the theoretically expected number. According to the classification accuracy obtained, this last approach exhibited a higher class discriminating power. It was capable of finding significant differences in six out of seven experimental datasets-whereas the standard PE method only did in four-and it also had better classification accuracy. It can be concluded that using the additional information provided by the number of forbidden/found patterns, it is possible to achieve a higher discriminating power than using the classical PE normalisation method. The resulting algorithm is also very similar to that of PE and very easy to implement.
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Hussain L, Saeed S, Idris A, Awan IA, Shah SA, Majid A, Ahmed B, Chaudhary QA. Regression analysis for detecting epileptic seizure with different feature extracting strategies. BIOMED ENG-BIOMED TE 2019; 64:619-642. [PMID: 31145684 DOI: 10.1515/bmt-2018-0012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 01/08/2019] [Indexed: 11/15/2022]
Abstract
Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.
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Affiliation(s)
- Lal Hussain
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan, E-mail:
| | - Sharjil Saeed
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Adnan Idris
- Department of Computer Sciences and Information Technology, The University of Poonch, Rawalakot 12350, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Saeed Arif Shah
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan.,College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
| | - Abdul Majid
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Bilal Ahmed
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Quratul-Ain Chaudhary
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
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Hussain L, Aziz W, Alshdadi AA, Abbasi AA, Majid A, Marchal AR. Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps. Technol Health Care 2019; 28:259-273. [PMID: 31594269 DOI: 10.3233/thc-191803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain neural activity is measured using electroencephalography (EEG) recording from the scalp. The EEG motor/imagery tasks help disabled people to communicate with the external environment. OBJECTIVE In this paper, robust multiscale sample entropy (MSE) and wavelet entropy measures are employed using topographic maps' analysis and tabulated form to quantify the dynamics of EEG motor movements tasks with actual and imagery opening and closing of fist or feet movements. METHODS To distinguish these conditions, we used the topographic maps which visually show the significance level of the brain regions and probes for dominant activities. The paired t-test and Posthoc Tukey test are used to find the significance levels. RESULTS The topographic maps results obtained using MSE reveal that maximum electrodes show the significance in frontpolar, frontal, and few frontal and parietal brain regions at temporal scales 3, 4, 6 and 7. Moreover, it was also observed that the distribution of significance is from frontoparietal brain regions. Using wavelet entropy, the significant results are obtained at frontpolar, frontal, and few electrodes in right hemisphere. The highest significance is obtained at frontpolar electrodes followed by frontal and few central and parietal electrodes.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Wajid Aziz
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan.,College of Computer Sciences and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Abdulrahman A Alshdadi
- College of Computer Sciences and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Adeel Ahmed Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Abdul Majid
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Ali Raza Marchal
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
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Thuraisingham RA. Examining nonlinearity using complexity and entropy. CHAOS (WOODBURY, N.Y.) 2019; 29:063109. [PMID: 31266329 DOI: 10.1063/1.5096903] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 05/28/2019] [Indexed: 06/09/2023]
Abstract
A method based on complexity and Shannon entropy along with surrogate data testing is described to detect nonlinearity in biosignals. The importance of denoising is illustrated in the detection of nonlinearity. The procedure is tested on synthetic linear and Lorenz data and on a large set of surface and intracranial electroencephalographic (EEG) signals. This method provides a measure of the complexity and entropy associated with nonlinearity. The results indicate that EEG signals measured during a seizure and from intracranial recordings show more nonlinearity when compared with surface EEG data and eyes open more than eyes closed signals.
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Affiliation(s)
- R A Thuraisingham
- Sydney Medical School, University of Sydney, 1A Russell Street, Eastwood, Sydney, New South Wales 2122, Australia
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Hussain L, Ahmed A, Saeed S, Rathore S, Awan IA, Shah SA, Majid A, Idris A, Awan AA. Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies. Cancer Biomark 2018; 21:393-413. [PMID: 29226857 DOI: 10.3233/cbm-170643] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.
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Affiliation(s)
- Lal Hussain
- QEC, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan.,Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Adeel Ahmed
- Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Sharjil Saeed
- Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Saima Rathore
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Imtiaz Ahmed Awan
- Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Saeed Arif Shah
- Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Abdul Majid
- Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Adnan Idris
- Department of CS and IT, University of Poonch Rawalakot, Rawalakot, Azad Kashmir, Pakistan
| | - Anees Ahmed Awan
- Department of CS and IT, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
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Hussain L, Aziz W, Saeed S, Shah SA, Nadeem MSA, Awan IA, Abbas A, Majid A, Kazmi SZH. Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm. ACTA ACUST UNITED AC 2018; 63:481-490. [DOI: 10.1515/bmt-2017-0041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 06/21/2017] [Indexed: 12/12/2022]
Abstract
Abstract
In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.
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Hussain L. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach. Cogn Neurodyn 2018; 12:271-294. [PMID: 29765477 PMCID: PMC5943212 DOI: 10.1007/s11571-018-9477-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 12/01/2017] [Accepted: 01/18/2018] [Indexed: 01/08/2023] Open
Abstract
Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.
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Affiliation(s)
- Lal Hussain
- Quality Enhancement Cell (QEC), The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, Azad Kashmir 13100 Pakistan
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
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Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques. ARCHIVES OF NEUROSCIENCE 2018. [DOI: 10.5812/archneurosci.61161] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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