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Torres-Ferrus M, López-Veloso AC, Gonzalez-Quintanilla V, González-García N, Díaz de Teran J, Gago-Veiga A, Camiña J, Ruiz M, Mas-Sala N, Bohórquez S, Gallardo VJ, Pozo-Rosich P. The MIGREX study: Prevalence and risk factors of sexual dysfunction among migraine patients. Neurologia 2023; 38:541-549. [PMID: 37802552 DOI: 10.1016/j.nrleng.2021.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/07/2021] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Migraine attacks have a high impact on daily activities. There is limited research on the burden of migraine on sexual functioning. OBJECTIVE To determine the prevalence of sexual dysfunction in patients with migraine and its relationship with migraine features and comorbidities. METHOD This is a cross-sectional study. We included migraine patients between 18 and 60 years-old from 8 Headache Clinics in Spain. We recorded demographic data and migraine features. Patients fulfilled a survey including comorbidities, Arizona Sexual Experiences Scale, Hospital Anxiety and Depression Scale and a questionnaire about migraine impact on sexual activity. A K-nearest neighbor supervised learning algorithm was used to identify differences between migraine patients with and without sexual dysfunction. RESULTS We included 306 patients (85.6% women, mean age 42.3±11.1 years). A 41.8% of participants had sexual dysfunction. Sexual dysfunction was associated with being female (OR [95% CI]: 2.42 [1.17-5.00]; p<0.001), being older than 46.5 years (4.04 [2.48-6.59]; p<0.001), having chronic migraine (2.31 [1.41-3.77]; p=0.001), using preventive medication (2.45 [1.35-4.45]; p=0.004), analgesic overusing (3.51 [2.03-6.07]; p<0.001), menopause (4.18 [2.43-7.17]; p<0.001) and anxiety (2.90 [1.80-4.67]; p<0.001) and depression (6.14 [3.18-11.83]; p<0.001). However, only female gender, age, menopause and depression were the statistically significant variables selected in the model to classify migraine patients with or without sexual dysfunction (Accuracy [95% CI]: 0.75 (0.62-0.85), Kappa: 0.48, p=0.005). CONCLUSIONS Sexual dysfunction is frequent in migraine patients visited in a headache clinic. However, migraine characteristics or use of preventive medication are not directly associated with sexual dysfunction. Instead, risk factors for sexual dysfunction were female gender, higher age, menopause and depression.
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Affiliation(s)
- M Torres-Ferrus
- Neurology Department, Vall d'Hebron University Hospital, Barcelona, Spain.
| | - A C López-Veloso
- Neurology Department, Gran Canaria Dr. Negrín University Hospital, Las Palmas de Gran Canaria, Spain
| | | | | | - J Díaz de Teran
- Neurology Department, La Paz University Hospital, Madrid, Spain
| | - A Gago-Veiga
- Neurology Department, La Princesa University Hospital, Madrid, Spain
| | - J Camiña
- Neurology Department, Rotger Clinic, Palma de Mallorca, Spain
| | - M Ruiz
- Neurology Department, San Juan Hospital, Alicante, Spain
| | - N Mas-Sala
- Neurology Department, Althaia Hospital, Red Asistencial Universitaria de Manresa, Spain
| | - S Bohórquez
- Neurology Department, Sabana University, Bogotá, Colombia
| | - V J Gallardo
- Neurology Department, Sabana University, Bogotá, Colombia
| | - P Pozo-Rosich
- Neurology Department, Vall d'Hebron University Hospital, Barcelona, Spain
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2
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Ji H, Xu T, Xue T, Xu T, Yan Z, Liu Y, Chen B, Jiang W. An effective fusion model for seizure prediction: GAMRNN. Front Neurosci 2023; 17:1246995. [PMID: 37674519 PMCID: PMC10477703 DOI: 10.3389/fnins.2023.1246995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 07/24/2023] [Indexed: 09/08/2023] Open
Abstract
The early prediction of epileptic seizures holds paramount significance in patient care and medical research. Extracting useful spatial-temporal features to facilitate seizure prediction represents a primary challenge in this field. This study proposes GAMRNN, a novel methodology integrating a dual-layer gated recurrent unit (GRU) model with a convolutional attention module. GAMRNN aims to capture intricate spatial-temporal characteristics by highlighting informative feature channels and spatial pattern dynamics. We employ the Lion optimization algorithm to enhance the model's generalization capability and predictive accuracy. Our evaluation of GAMRNN on the widely utilized CHB-MIT EEG dataset demonstrates its effectiveness in seizure prediction. The results include an impressive average classification accuracy of 91.73%, sensitivity of 88.09%, specificity of 92.09%, and a low false positive rate of 0.053/h. Notably, GAMRNN enables early seizure prediction with a lead time ranging from 5 to 35 min, exhibiting remarkable performance improvements compared to similar prediction models.
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Affiliation(s)
- Hong Ji
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Ting Xu
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xue
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xu
- School of Software, Northwestern Polytechnical University, Xi'an, China
| | - Zhiqiang Yan
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yonghong Liu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Badong Chen
- Institute of Artistic Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
| | - Wen Jiang
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
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3
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Jiang X, Liu X, Liu Y, Wang Q, Li B, Zhang L. Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis. Front Neurosci 2023; 17:1191683. [PMID: 37260846 PMCID: PMC10228742 DOI: 10.3389/fnins.2023.1191683] [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: 03/22/2023] [Accepted: 04/14/2023] [Indexed: 06/02/2023] Open
Abstract
Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.
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Affiliation(s)
- Ximiao Jiang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Xiaotong Liu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Youjun Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Liyuan Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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4
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Al-hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-48. [PMID: 37359338 PMCID: PMC10123593 DOI: 10.1007/s11227-023-05299-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today's world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people's lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.
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Affiliation(s)
| | - Ali Kadhum M. Al-Qurabat
- Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq
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5
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Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges. Diagnostics (Basel) 2023; 13:diagnostics13061058. [PMID: 36980366 PMCID: PMC10047386 DOI: 10.3390/diagnostics13061058] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 03/14/2023] Open
Abstract
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.
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6
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Singh YP, Lobiyal D. Automatic prediction of epileptic seizure using hybrid deep ResNet-LSTM model. AI COMMUN 2023. [DOI: 10.3233/aic-220177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Numerous advanced data processing and machine learning techniques for identifying epileptic seizures have been developed in the last two decades. Nonetheless, many of these solutions need massive data sets and intricate computations. Our approach transforms electroencephalogram (EEG) data into the time-frequency domain by utilizing a short-time fourier transform (STFT) and the spectrogram (t-f) images as the input stage of the deep learning model. Using EEG data, we have constructed a hybrid model comprising of a Deep Convolution Network (ResNet50) and a Long Short-Term Memory (LSTM) for predicting epileptic seizures. Spectrogram images are used to train the proposed hybrid model for feature extraction and classification. We analyzed the CHB-MIT scalp EEG dataset. For each preictal period of 5, 15, and 30 minutes, experiments are conducted to evaluate the performance of the proposed model. The experimental results indicate that the proposed model produced the optimum performance with a 5-minute preictal duration. We achieved an average accuracy of 94.5%, the average sensitivity of 93.7%, the f1-score of 0.9376, and the average false positive rate (FPR) of 0.055. Our proposed technique surpassed the random predictor and other current algorithms used for seizure prediction for all patients’ data in the dataset. One can use the effectiveness of our proposed model to help in the early diagnosis of epilepsy and provide early treatment.
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Affiliation(s)
| | - D.K. Lobiyal
- School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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7
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Yang J, Yee PL, Khan AA, Karamti H, Eldin ET, Aldweesh A, Jery AE, Hussain L, Omar A. Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features. Digit Health 2023; 9:20552076231172632. [PMID: 37256015 PMCID: PMC10226179 DOI: 10.1177/20552076231172632] [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: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/01/2023] Open
Abstract
Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. 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 lung cancer.
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Affiliation(s)
- Jing Yang
- Faculty of Computer Science and
Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Por Lip Yee
- Faculty of Computer Science and
Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Abdullah Ayub Khan
- Department of Computer Science and
Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi,
Pakistan
| | - Hanen Karamti
- Department of Computer Sciences,
College of Computer and Information Sciences, Princess Nourah bint Abdulrahman
University, Riyadh, Saudi Arabia
| | - Elsayed Tag Eldin
- Faculty of Engineering and Technology, Future University in Egypt, New Cairo, Cairo, Egypt
| | - Amjad Aldweesh
- College of Computer Science and
Information Technology, Shaqra University, Shaqra, Saudi Arabia
| | - Atef El Jery
- Department of Chemical Engineering,
College of Engineering, King Khalid University, Abha, Saudi Arabia
- National Engineering School of Gabes,
Gabes University, Zrig Gabes, Tunisia
| | - Lal Hussain
- Department of Computer Science and
Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu
and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
- Department of Computer Science and
Information Technology, University of Azad Jammu and Kashmir, Athmuqam, Azad
Kashmir, Pakistan
| | - Abdulfattah Omar
- Department of English, College of
Science & Humanities, Prince Sattam Bin Abdulaziz
University, Al-Kharj, Saudi Arabia
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8
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Escobar-Ipuz F, Torres A, García-Jiménez M, Basar C, Cascón J, Mateo J. Prediction of patients with idiopathic generalized epilepsy from healthy controls using machine learning from scalp EEG recordings. Brain Res 2022; 1798:148131. [DOI: 10.1016/j.brainres.2022.148131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/14/2022] [Accepted: 10/23/2022] [Indexed: 11/05/2022]
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9
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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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10
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Hybrid metaheuristic algorithm enhanced support vector machine for epileptic seizure detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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11
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Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136517] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.
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12
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Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals. SENSORS 2022; 22:s22083066. [PMID: 35459052 PMCID: PMC9031940 DOI: 10.3390/s22083066] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/07/2022] [Accepted: 04/13/2022] [Indexed: 02/01/2023]
Abstract
Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest.
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13
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Natu M, Bachute M, Gite S, Kotecha K, Vidyarthi A. Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7751263. [PMID: 35096136 PMCID: PMC8794701 DOI: 10.1155/2022/7751263] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022]
Abstract
Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to "pops" in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.
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Affiliation(s)
- Milind Natu
- Department of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University), SIU, Lavale, Pune, Maharashtra, India
| | - Mrinal Bachute
- Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), SIU, Lavale, Pune, Maharashtra, India
| | - Shilpa Gite
- Computer Science and Information Technology Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
- Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University, Pune 412115, India
| | - Ketan Kotecha
- Computer Science and Information Technology Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
- Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University, Pune 412115, India
| | - Ankit Vidyarthi
- Department of CSE&IT, Jaypee Institute of Information Technology Noida, India
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14
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Deep-layer motif method for estimating information flow between EEG signals. Cogn Neurodyn 2022; 16:819-831. [PMID: 35847539 PMCID: PMC9279550 DOI: 10.1007/s11571-021-09759-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 10/04/2021] [Accepted: 11/22/2021] [Indexed: 11/26/2022] Open
Abstract
Accurate identification for the information flow between epileptic seizure signals is the key to construct the directional epileptic brain network which can be used to localize epileptic focus. In this paper, our concern is on how to improve the direction identification of information flow and also investigate how it can be cut off or weakened. In view of this, we propose the deep-layer motif method. Based on the directional index (DI) estimation using permutation conditional mutual information, the effectiveness of the proposed deep-layer motif method is numerically assessed with the coupled mass neural model. Furthermore, we investigate the robustness of this method in considering the interference of autaptic coupling, time delay and short-term plasticity. Results show that compared to the simple 1-layer motif method, the 2nd- and 3rd-layer motif methods have the dominant enhancement effects for the direction identification. In particular, deep-layer motif method possesses good anti-jamming performance and good robustness in calculating DI. In addition, we investigate the effect of deep brain stimulation (DBS) on the information flow. It is found that this deep-layer motif method is still superior to the single-layer motif method in direction identification and is robust to weak DBS. However, the high-frequency strong DBS can effectively decrease the DI suggesting the weakened information flow. These results may give new insights into the seizure detection and control.
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15
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韩 长, 彭 福, 陈 财, 李 文, 张 昔, 王 星, 周 卫. [Research progress of epileptic seizure predictions based on electroencephalogram signals]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:1193-1202. [PMID: 34970903 PMCID: PMC9927116 DOI: 10.7507/1001-5515.202105052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/15/2021] [Indexed: 06/14/2023]
Abstract
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
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Affiliation(s)
- 长明 韩
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
- 山东中科先进技术研究院有限公司(济南 250000)Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250000, P.R.China
| | - 福来 彭
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 财 陈
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 文超 李
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 昔坤 张
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 星维 王
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
| | - 卫东 周
- 山东大学 微电子学院(济南 250101)School of Microelectronics, Shandong University, Jinan 250101, P.R.China
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LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals. Cogn Neurodyn 2021; 16:779-790. [PMID: 35847545 PMCID: PMC9279545 DOI: 10.1007/s11571-021-09748-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/23/2021] [Accepted: 11/03/2021] [Indexed: 11/20/2022] Open
Abstract
Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.
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Patel V, Tailor J, Ganatra A. Essentials of Predicting Epileptic Seizures Based on EEG Using Machine Learning: A Review. Open Biomed Eng J 2021. [DOI: 10.2174/1874120702115010090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Objective:
Epilepsy is one of the chronic diseases, which requires exceptional attention. The unpredictability of the seizures makes it worse for a person suffering from epilepsy.
Methods:
The challenge to predict seizures using modern machine learning algorithms and computing resources would be a boon to a person with epilepsy and its caregivers. Researchers have shown great interest in the task of epileptic seizure prediction for a few decades. However, the results obtained have not clinical applicability because of the high false-positive ratio. The lack of standard practices in the field of epileptic seizure prediction makes it challenging for novice ones to follow the research. The chances of reproducibility of the result are negligible due to the unavailability of implementation environment-related details, use of standard datasets, and evaluation parameters.
Results:
Work here presents the essential components required for the prediction of epileptic seizures, which includes the basics of epilepsy, its treatment, and the need for seizure prediction algorithms. It also gives a detailed comparative analysis of datasets used by different researchers, tools and technologies used, different machine learning algorithm considerations, and evaluation parameters.
Conclusion:
The main goal of this paper is to synthesize different methodologies for creating a broad view of the state-of-the-art in the field of seizure prediction.
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Murugappan M, Zheng BS, Khairunizam W. Recurrent Quantification Analysis-Based Emotion Classification in Stroke Using Electroencephalogram Signals. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05369-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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BENCHAIB YASMINE. IMPROVED ARTIFICIAL NEURAL NETWORK FOR EPILEPTIC SEIZURES DETECTION. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electroencephalogram (EEG) is a fundamental and unique tool for exploring human brain activity in general and epileptic mechanism in particular. It offers significant information about epileptic seizures source known as epileptogenic area. However, it is often complicated to detect critical changes in EEG signals by visual examination, since this signal aspect of epileptic persons seems to be normal out of the seizure. Thus, the challenge is to design such a robust and automatic system to detect these unseen changes and use them for diagnosis. In this research, we apply the Artificial Metaplasticity Multi-Layer Perceptron (AMMLP) together with discrete wavelet transform (DWT) to Bonn EEG signals for seizure detection goal. Significant features were then extracted from the well-known EEG brainwaves. Aiming to decrease the computational time and improve classification accuracy, we performed a features ranking and selection employing the Relief algorithm. The obtained AMMLP classification accuracy of 98.97% proved the effctiveness of the applied approach. Our results were compared to recent researches results on the same database, proving to be superior or at least an interesting alternative for seizures detection within EEG signals.
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Affiliation(s)
- YASMINE BENCHAIB
- Biomedical Engineering Department, Faculty of Technology, University of Tlemcen, Algeria
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Thara DK, Premasudha BG, Nayak RS, Murthy TV, Ananth Prabhu G, Hanoon N. Electroencephalogram for epileptic seizure detection using stacked bidirectional LSTM_GAP neural network. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00459-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Torres-Ferrus M, López-Veloso AC, Gonzalez-Quintanilla V, González-García N, Díaz de Teran J, Gago-Veiga A, Camiña J, Ruiz M, Mas-Sala N, Bohórquez S, Gallardo VJ, Pozo-Rosich P. The MIGREX study: Prevalence and risk factors of sexual dysfunction among migraine patients. Neurologia 2021; 38:S0213-4853(21)00036-0. [PMID: 33766414 DOI: 10.1016/j.nrl.2021.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/04/2021] [Accepted: 02/07/2021] [Indexed: 10/21/2022] Open
Abstract
BACKGROUND Migraine attacks have a high impact on daily activities. There is limited research on the burden of migraine on sexual functioning. OBJECTIVE To determine the prevalence of sexual dysfunction in patients with migraine and its relationship with migraine features and comorbidities. METHOD This is a cross-sectional study. We included migraine patients between 18 and 60 years-old from 8 Headache Clinics in Spain. We recorded demographic data and migraine features. Patients fulfilled a survey including comorbidities, Arizona Sexual Experiences Scale, Hospital Anxiety and Depression Scale and a questionnaire about migraine impact on sexual activity. A K-nearest neighbor supervised learning algorithm was used to identify differences between migraine patients with and without sexual dysfunction. RESULTS We included 306 patients (85.6% women, mean age 42.3±11.1 years). A 41.8% of participants had sexual dysfunction. Sexual dysfunction was associated with being female (OR [95% CI]: 2.42 [1.17-5.00]; p<0.001), being older than 46.5 years (4.04 [2.48-6.59]; p<0.001), having chronic migraine (2.31 [1.41-3.77]; p=0.001), using preventive medication (2.45 [1.35-4.45]; p=0.004), analgesic overusing (3.51 [2.03-6.07]; p<0.001), menopause (4.18 [2.43-7.17]; p<0.001) and anxiety (2.90 [1.80-4.67]; p<0.001) and depression (6.14 [3.18-11.83]; p<0.001). However, only female gender, age, menopause and depression were the statistically significant variables selected in the model to classify migraine patients with or without sexual dysfunction (Accuracy [95% CI]: 0.75 (0.62-0.85), Kappa: 0.48, p=0.005). CONCLUSIONS Sexual dysfunction is frequent in migraine patients visited in a headache clinic. However, migraine characteristics or use of preventive medication are not directly associated with sexual dysfunction. Instead, risk factors for sexual dysfunction were female gender, higher age, menopause and depression.
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Affiliation(s)
- M Torres-Ferrus
- Neurology Department, Vall d'Hebron University Hospital, Barcelona, Spain.
| | - A C López-Veloso
- Neurology Department, Gran Canaria Dr. Negrín University Hospital, Las Palmas de Gran Canaria, Spain
| | | | | | - J Díaz de Teran
- Neurology Department, La Paz University Hospital, Madrid, Spain
| | - A Gago-Veiga
- Neurology Department, La Princesa University Hospital, Madrid, Spain
| | - J Camiña
- Neurology Department, Rotger Clinic, Palma de Mallorca, Spain
| | - M Ruiz
- Neurology Department, San Juan Hospital, Alicante, Spain
| | - N Mas-Sala
- Neurology Department, Althaia Hospital, Red Asistencial Universitaria de Manresa, Spain
| | - S Bohórquez
- Neurology Department, Sabana University, Bogotá, Colombia
| | - V J Gallardo
- Neurology Department, Sabana University, Bogotá, Colombia
| | - P Pozo-Rosich
- Neurology Department, Vall d'Hebron University Hospital, Barcelona, Spain
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Behnoush B, Bazmi E, Nazari SH, Khodakarim S, Looha MA, Soori H. Machine learning algorithms to predict seizure due to acute tramadol poisoning. Hum Exp Toxicol 2021; 40:1225-1233. [PMID: 33538187 DOI: 10.1177/0960327121991910] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making. METHODS Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013-2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models. RESULTS In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models. CONCLUSION A perfect prediction model may help improve clinicians' decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.
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Affiliation(s)
- B Behnoush
- Department of Forensic Medicine, 48439Tehran University of Medical Sciences, Tehran, Iran
| | - E Bazmi
- Department of Epidemiology, 216617School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Legal Medicine Research Center, Legal Medicine Organization, Tehran, Iran
| | - S H Nazari
- Department of Epidemiology, 216617School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - S Khodakarim
- Department of Epidemiology, 216617School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - M A Looha
- Department of Biostatistics, Faculty of Paramedical Sciences, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - H Soori
- Department of Epidemiology, 216617School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Safety Promotion and Injury Prevention Research Center, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Sharp decrease in the Laplacian matrix rank of phase-space graphs: a potential biomarker in epilepsy. Cogn Neurodyn 2021; 15:649-659. [PMID: 34367366 DOI: 10.1007/s11571-020-09662-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 11/01/2020] [Accepted: 12/29/2020] [Indexed: 10/22/2022] Open
Abstract
In this paper, phase space reconstruction from stereo-electroencephalography data of ten patients with focal epilepsy forms a series of graphs. Those obtained graphs reflect the transition characteristics of brain dynamical system from pre-seizure to seizure of epilepsy. Interestingly, it is found that the rank of Laplacian matrix of these graphs has a sharp decrease when a seizure is close to happen, which thus might be viewed as a new potential biomarker in epilepsy. In addition, the reliability of this method is numerically verified with a coupled mass neural model. In particular, our simulation suggests that this potential biomarker can play the roles of predictive effect or delayed awareness, depending on the bias current of the Gaussian noise. These results may give new insights into the seizure detection.
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Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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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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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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Bazmi E, Behnoush B, Hashemi Nazari S, Khodakarim S, Behnoush AH, Soori H. Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2020; 8:e59. [PMID: 32613201 PMCID: PMC7305636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Seizure is a common complication of tramadol poisoning and predicting it will help clinicians in preventing seizure and better management of patients. This study aimed to develop and validate a prediction model to assess the risk of seizure in acute tramadol poisoning. METHODS This retrospective observational study was conducted on 909 patients with acute tramadol poisoning in Baharloo Hospital, Tehran, Iran, (2015-2019). Several available demographic, clinical, and para-clinical characteristics were considered as potential predictors of seizure and extracted from clinical records. The data were split into derivation and validation sets (70/30 split) via random sampling. Derivation set was used to develop a multivariable logistic regression model. The model was tested on the validation set and its performance was assessed with receiver operating characteristic (ROC) curve. RESULTS The mean (standard deviation (SD)) of patients' age was 23.75 (7.47) years and 683 (75.1%) of them were male. Seizures occurred in 541 (60%) patients. Univariate analysis indicated that sex, pulse rate (PR), arterial blood Carbone dioxide pressure (PCO2), Glasgow Coma Scale (GCS), blood bicarbonate level, pH, and serum sodium level could predict the chance of seizure in acute tramadol poisoning. The final model in derivation set consisted of sex, PR, GCS, pH, and blood bicarbonate level. The model showed good accuracy on the validation set with an area under the ROC curve of 0.77 (95% CI: 0.67-0.87). CONCLUSION Representation of this model as a decision tree could help clinicians to identify high-risk patients with tramadol poisoning-induced seizure and in decision-making at triage of emergency departments in hospitals.
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Affiliation(s)
- Elham Bazmi
- Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Behnam Behnoush
- Department of Forensic Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Saeed Hashemi Nazari
- Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Soheila Khodakarim
- Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | | | - Hamid Soori
- Safety Promotion and Injury Prevention Research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Si Y. Machine learning applications for electroencephalograph signals in epilepsy: a quick review. ACTA EPILEPTOLOGICA 2020. [DOI: 10.1186/s42494-020-00014-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
AbstractMachine learning (ML) is a fundamental concept in the field of state-of-the-art artificial intelligence (AI). Over the past two decades, it has evolved rapidly and been employed wildly in many fields. In medicine the widespread usage of ML has been observed in recent years. The present review examines various ML approaches for electroencephalograph (EEG) signal procession in epilepsy research, highlighting applications in the aspect of automated seizure detection, prediction and orientation. The present review also presents advantage, challenge and future direction of ML techniques in the analysis of EEG signals in epilepsy.
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Čukić M, Stokić M, Simić S, Pokrajac D. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cogn Neurodyn 2020; 14:443-455. [PMID: 32655709 DOI: 10.1007/s11571-020-09581-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 02/18/2020] [Accepted: 03/06/2020] [Indexed: 01/05/2023] Open
Abstract
Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.
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Affiliation(s)
- Milena Čukić
- Department for General Physiology and Biophysics, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, 11 000 Serbia
- Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, Madrid, Spain
| | - Miodrag Stokić
- Life Activities Advancement Center, Gospodar Jovanova 35, Belgrade, 11 000 Serbia
- Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Slobodan Simić
- Institute for Mental Health, Palmotićeva 37, Belgrade, Serbia
| | - Dragoljub Pokrajac
- Delaware Biotechnology Institute, Delaware State University, 305D Science Center North, 1200 N Dupont Hwy, Dover, DE 19901 USA
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de Oliveira JL, Ávila M, Martins TC, Alvarez-Silva M, Winkelmann-Duarte EC, Salgado ASI, Cidral-Filho FJ, Reed WR, Martins DF. Medium- and long-term functional behavior evaluations in an experimental focal ischemic stroke mouse model. Cogn Neurodyn 2020; 14:473-481. [PMID: 32655711 DOI: 10.1007/s11571-020-09584-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 02/25/2020] [Accepted: 03/11/2020] [Indexed: 12/16/2022] Open
Abstract
Cerebrovascular accident (CVA) is one of the leading causes of death and disability worldwide, as well as a major financial burden for health care systems. CVA rodent models provide experimental support to determine possible in vivo therapies to reduce brain injury and consequent sequelae. This study analyzed nociceptive, motor, cognitive and mood functions in mice submitted to distal middle cerebral artery (DMCA) occlusion. Male C57BL mice (n = 8) were randomly allocated to control or DMCA groups. Motor function was evaluated with the tests: grip force, rotarod and open field; and nociceptive threshold with von Frey and hot plate assessments. Cognitive function was evaluated with the inhibitory avoidance test, and mood with the tail suspension test. Evaluations were conducted on the seventh- and twenty-eighth-day post DMCA occlusion to assess medium- and long-term effects of the injury, respectively. DMCA occlusion significantly decreases muscle strength and spontaneous locomotion (p < 0.05) both medium- and long term; as well as increases immobility in the tail-suspension test (p < 0.05), suggesting a depressive-type behavior. However, DMCA occlusion did not affect nociceptive threshold nor cognitive functions (p > 0.05). These results suggest that, medium- and long-term effects of DMCA occlusion include motor function impairments, but no sensory dysfunction. Additionally, the injury affected mood but did not hinder cognitive function.
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Affiliation(s)
- Juçara Loli de Oliveira
- Department of Morphological Sciences, Federal University of Santa Catarina, Santa Catarina, Brazil
| | - Marina Ávila
- Experimental Neuroscience Laboratory (LaNEx) and Postgraduate Program in Health Sciences, University of Southern Santa Catarina at Palhoça, 25 Pedra Branca Avenue, Santa Catarina, Brazil
| | - Thiago Cesar Martins
- Experimental Neuroscience Laboratory (LaNEx) and Postgraduate Program in Health Sciences, University of Southern Santa Catarina at Palhoça, 25 Pedra Branca Avenue, Santa Catarina, Brazil
| | - Marcio Alvarez-Silva
- Stem Cell and Bioengineering Laboratory, Department of Cell Biology, Embryology and Genetics, Federal University of Santa Catarina, Santa Catarina, Brazil
| | | | - Afonso Shiguemi Inoue Salgado
- Experimental Neuroscience Laboratory (LaNEx) and Postgraduate Program in Health Sciences, University of Southern Santa Catarina at Palhoça, 25 Pedra Branca Avenue, Santa Catarina, Brazil.,Postgraduate Program in Health Sciences, University of Southern Santa Catarina, Santa Catarina, Brazil.,Coordinator of Integrative Physical Therapy Residency, Philadelphia University Center, Londrina, PR Brazil
| | - Francisco José Cidral-Filho
- Experimental Neuroscience Laboratory (LaNEx) and Postgraduate Program in Health Sciences, University of Southern Santa Catarina at Palhoça, 25 Pedra Branca Avenue, Santa Catarina, Brazil.,Postgraduate Program in Health Sciences, University of Southern Santa Catarina, Santa Catarina, Brazil
| | - William R Reed
- Department of Physical Therapy, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL USA
| | - Daniel F Martins
- Experimental Neuroscience Laboratory (LaNEx) and Postgraduate Program in Health Sciences, University of Southern Santa Catarina at Palhoça, 25 Pedra Branca Avenue, Santa Catarina, Brazil.,Postgraduate Program in Health Sciences, University of Southern Santa Catarina, Santa Catarina, Brazil
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Classification of epileptic seizure dataset using different machine learning algorithms. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100444] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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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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Hejazi M, Motie Nasrabadi A. Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn 2019; 13:461-473. [PMID: 31565091 DOI: 10.1007/s11571-019-09534-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 02/07/2019] [Accepted: 04/25/2019] [Indexed: 01/09/2023] Open
Abstract
Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.
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Affiliation(s)
- Mona Hejazi
- 1Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Ali Motie Nasrabadi
- 2Department of Biomedical Engineering, Faculty of Biomedical Engineering, Shahed University, Tehran, Iran
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Li F, Liang Y, Zhang L, Yi C, Liao Y, Jiang Y, Si Y, Zhang Y, Yao D, Yu L, Xu P. Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis. Cogn Neurodyn 2019; 13:175-181. [PMID: 30956721 DOI: 10.1007/s11571-018-09517-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 11/29/2018] [Accepted: 12/19/2018] [Indexed: 11/29/2022] Open
Abstract
Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.
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Affiliation(s)
- Fali Li
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi Liang
- 2Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.,3Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
| | - Luyan Zhang
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Chanlin Yi
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liao
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanling Jiang
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yajing Si
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yangsong Zhang
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,4School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China
| | - Dezhong Yao
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,5School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- 2Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.,3Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
| | - Peng Xu
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,5School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Goshvarpour A, Goshvarpour A. EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences. Cogn Neurodyn 2018; 13:161-173. [PMID: 30956720 DOI: 10.1007/s11571-018-9516-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/07/2018] [Accepted: 12/11/2018] [Indexed: 12/14/2022] Open
Abstract
Previously, gender-specific affective responses have been shown using neurophysiological signals. The present study intended to compare the differences in electroencephalographic (EEG) power spectra and EEG brain sources between men and women during the exposure of affective music video stimuli. The multi-channel EEG signals of 15 males and 15 females available in the database for emotion analysis using physiological signals were studied, while subjects were watching sad, depressing, and fun music videos. Seven EEG frequency bands were computed using average Fourier cross-spectral matrices. Then, standardized low-resolution electromagnetic tomography (sLORETA) was used to localize regions involved specifically in these emotional responses. To evaluate gender differences, independent sample t test was calculated for the sLORETA source powers. Our results showed that (1) the mean EEG power for all frequency bands in the women's group was significantly higher than that of the men's group; (2) spatial distribution differentiation between men and women was detected in all EEG frequency bands. (3) This difference has been related to the emotional stimuli, which was more evident for negative emotions. Taken together, our results showed that men and women recruited dissimilar brain networks for processing sad, depressing, and fun audio-visual stimuli.
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Affiliation(s)
- Atefeh Goshvarpour
- 1Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- 2Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan Iran
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