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Gundogdu S, Elbir T. Elevating Hourly PM 2.5 Forecasting in Istanbul, Türkiye: Leveraging ERA5 Reanalysis and Genetic Algorithms in a Comparative Machine Learning Model Analysis. CHEMOSPHERE 2024:143096. [PMID: 39146993 DOI: 10.1016/j.chemosphere.2024.143096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 08/07/2024] [Accepted: 08/13/2024] [Indexed: 08/17/2024]
Abstract
Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM2.5 predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA5 reanalysis data for a grid-based spatial analysis of Istanbul, Türkiye, a densely populated city with diverse pollutant sources. It assesses the predictive accuracy of advanced machine learning (ML) models-Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGB), Random Forest (RF), and Nonlinear Autoregressive with Exogenous Inputs (NARX). Notably, it introduces genetic algorithm optimization for the NARX model to enhance its performance. The models were trained on hourly PM2.5 concentrations from twenty monitoring stations across 2020-2021. Istanbul was divided into seven regions based on ERA5 grid distributions to examine PM2.5 spatial variability. Seventeen input variables from ERA5, including meteorological, land cover, and vegetation parameters, were analyzed using the Neighborhood Component Analysis (NCA) method to identify the most predictive variables. Comparative analysis showed that while all models provided valuable insights (RF>LGB>XGB>MLR), the NARX model outperformed them, particularly with the complex dataset used. The NARX model achieved a high R-value (0.89), low RMSE (5.24 μg/m³), and low MAE (2.94 μg/m³). It performed best in autumn and winter, with the highest accuracy in Region-1 (R-value 0.94) and the lowest in Region-5 (R-value 0.75). This study's success in a complex urban setting with limited monitoring underscores the robustness of the NARX model and the methodology's potential for global application in similar urban contexts. By addressing temporal and spatial variability in air quality predictions, this research sets a new benchmark and highlights the importance of advanced data analysis techniques for developing targeted pollution control strategies and public health policies.
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Affiliation(s)
- Serdar Gundogdu
- Department of Computer Technologies, Bergama Vocational School, Dokuz Eylul University, Bergama-Izmir 35700, Türkiye.
| | - Tolga Elbir
- Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Buca-Izmir 35390, Türkiye; Dokuz Eylul University, Environmental Research and Application Center (ÇEVMER), Tinaztepe Campus, 35390 Buca/Izmir - Türkiye.
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2
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Davarinia F, Maleki A. Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets. Med Eng Phys 2024; 130:104198. [PMID: 39160026 DOI: 10.1016/j.medengphy.2024.104198] [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: 09/22/2023] [Revised: 05/20/2024] [Accepted: 06/25/2024] [Indexed: 08/21/2024]
Abstract
Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of handcrafted features was mined from windows of electromyogram (EMG) of the upper-limb muscles while reaching nine nearby targets like activities of daily living. The feature selection-based scoring method, neighborhood component analysis (NCA), selected the relevant feature subset. Finally, the target was recognized by the support vector machine (SVM) model. The classification performance was generalized by a nested cross-validation structure that selected the optimal feature subset in the inner loop. According to the low spatial resolution of the target location on display and following the slight discrimination of signals between targets, the best classification accuracy of 77.11 % was achieved for concatenating the features of two segments with a length of 2 and 0.25 s. Due to the lack of subtle variation in EMG, while reaching different targets, a wide range of features was applied to consider additional aspects of the knowledge contained in EMG signals. Furthermore, since NCA selected features that provided more discriminant power, it became achievable to employ various combinations of features and even concatenated features extracted from different movement parts to improve classification performance.
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Affiliation(s)
| | - Ali Maleki
- Biomedical Engineering Department, Semnan University, Semnan, Iran.
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3
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Cai Y, Wang C, Yuan H, Guo Y, Cho JH, Xing X, Jia Y. Exploring negative thermal expansion materials with bulk framework structures and their relevant scaling relationships through multi-step machine learning. MATERIALS HORIZONS 2024; 11:2914-2925. [PMID: 38567484 DOI: 10.1039/d3mh01509b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Discovering new negative thermal expansion (NTE) materials is a great challenge in experiment. Meanwhile, the machine learning (ML) method can be another approach to explore NTE materials using the existing material databases. Herein, we adopt the multi-step ML method with efficient data augmentation and cross-validation to identify around 1000 materials, including oxides, fluorides, and cyanides, with bulk framework structures as new potential NTE candidate materials from ICSD and other databases. Their corresponding coefficients of negative thermal expansion (CNTE) and temperature ranges are also well predicted. Among them, about 57 materials are predicted to have an NTE probability of 100%. Some predicted NTE materials were tested by the first-principles calculations with quasi-harmonic approximation (QHA), which indicates that the ML results are in good agreement with the first principles calculation results. Based on the comprehensive analysis of the existing and predicted NTE materials, we established three universal relationships of CNTE with an average electronegativity, porosity, and temperature range. From these, we also identified some important critical values characterizing the NTE property, which can serve as an important criterion for designing new NTE materials.
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Affiliation(s)
- Yu Cai
- Key Laboratory for Special Functional Materials of Ministry of Education, and School of Materials and Engineering, Henan University, Kaifeng 475001, China.
- Institute of Quantum Materials and Physics, Henan Academy of Sciences, Zhengzhou 450046, China
| | - Chunyan Wang
- Key Laboratory for Special Functional Materials of Ministry of Education, and School of Materials and Engineering, Henan University, Kaifeng 475001, China.
- Institute of Quantum Materials and Physics, Henan Academy of Sciences, Zhengzhou 450046, China
- School of Physics and Telecommunication Engineering, Zhoukou Normal University, Zhoukou 466001, China
| | - Huanli Yuan
- School of Physics and Telecommunication Engineering, Zhoukou Normal University, Zhoukou 466001, China
| | - Yuan Guo
- Key Laboratory for Special Functional Materials of Ministry of Education, and School of Materials and Engineering, Henan University, Kaifeng 475001, China.
- Institute of Solid States Chemistry, University of Science and Technology Beijing, Beijing 100083, China.
| | - Jun-Hyung Cho
- Department of Physics and Research Institute for Natural Science, Hanyang University, 222 Wangsimni-ro, Seongdong-Ku, Seoul 04763, Republic of Korea
| | - Xianran Xing
- Institute of Solid States Chemistry, University of Science and Technology Beijing, Beijing 100083, China.
| | - Yu Jia
- Key Laboratory for Special Functional Materials of Ministry of Education, and School of Materials and Engineering, Henan University, Kaifeng 475001, China.
- Institute of Quantum Materials and Physics, Henan Academy of Sciences, Zhengzhou 450046, China
- Joint center for Theoretical Physics, and School of Physics and Electronics, Henan University, Kaifeng 475001, China
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4
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Atila O, Deniz E, Ari A, Sengur A, Chakraborty S, Barua PD, Acharya UR. LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:7032. [PMID: 37631569 PMCID: PMC10459515 DOI: 10.3390/s23167032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/27/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023]
Abstract
Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.
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Affiliation(s)
- Orhan Atila
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, Turkey; (O.A.); (E.D.); (A.S.)
| | - Erkan Deniz
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, Turkey; (O.A.); (E.D.); (A.S.)
| | - Ali Ari
- Computer Engineering Department, Engineering Faculty, Inonu University, 44280 Malatya, Turkey;
| | - Abdulkadir Sengur
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, Turkey; (O.A.); (E.D.); (A.S.)
| | - Subrata Chakraborty
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Prabal Datta Barua
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- School of Information Systems, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - U. Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia;
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Aouragh AA, Bahaj M, Gherabi N. Comparative Study of Dimensionality Reduction Techniques and Machine Learning Algorithms for Alzheimer's Disease Classification and Prediction. 2022 IEEE 3RD INTERNATIONAL CONFERENCE ON ELECTRONICS, CONTROL, OPTIMIZATION AND COMPUTER SCIENCE (ICECOCS) 2022. [DOI: 10.1109/icecocs55148.2022.9983211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Abd Allah Aouragh
- Hassan 1st University,MIET Laboratory Faculty of Sciences and Techniques,Settat,Morocco
| | - Mohamed Bahaj
- Hassan 1st University,MIET Laboratory Faculty of Sciences and Techniques,Settat,Morocco
| | - Noreddine Gherabi
- Sultan Moulay Slimane University,LASTI Laboratory National School of Applied Sciences,Beni-Mellal,Morocco
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6
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Ma Z, Lu YY, Wang Y, Lin R, Yang Z, Zhang F, Wang Y. Metric learning for comparing genomic data with triplet network. Brief Bioinform 2022; 23:6679451. [DOI: 10.1093/bib/bbac345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/20/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Many biological applications are essentially pairwise comparison problems, such as evolutionary relationships on genomic sequences, contigs binning on metagenomic data, cell type identification on gene expression profiles of single-cells, etc. To make pair-wise comparison, it is necessary to adopt suitable dissimilarity metric. However, not all the metrics can be fully adapted to all possible biological applications. It is necessary to employ metric learning based on data adaptive to the application of interest. Therefore, in this study, we proposed MEtric Learning with Triplet network (MELT), which learns a nonlinear mapping from original space to the embedding space in order to keep similar data closer and dissimilar data far apart. MELT is a weakly supervised and data-driven comparison framework that offers more adaptive and accurate dissimilarity learned in the absence of the label information when the supervised methods are not applicable. We applied MELT in three typical applications of genomic data comparison, including hierarchical genomic sequences, longitudinal microbiome samples and longitudinal single-cell gene expression profiles, which have no distinctive grouping information. In the experiments, MELT demonstrated its empirical utility in comparison to many widely used dissimilarity metrics. And MELT is expected to accommodate a more extensive set of applications in large-scale genomic comparisons. MELT is available at https://github.com/Ying-Lab/MELT.
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Affiliation(s)
- Zhi Ma
- Department of Automation, Xiamen University , China
- National Institute for Data Science in Health and Medicine, Xiamen University
| | - Yang Young Lu
- Cheriton School of Computer Science, University of Waterloo , Waterloo, Ontario , Canada
| | - Yiwen Wang
- Department of Automation, Xiamen University , China
| | - Renhao Lin
- Department of Automation, Xiamen University , China
| | - Zizi Yang
- Department of Automation, Xiamen University , China
| | - Fang Zhang
- Cheriton School of Computer Science, University of Waterloo , Waterloo, Ontario , Canada
| | - Ying Wang
- Department of Automation, Xiamen University , China
- National Institute for Data Science in Health and Medicine, Xiamen University
- Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision , Xiamen, Fujian 361005 , China
- Fujian Key Laboratory of Genetics and Breeding of Marine Organisms , Xiamen, 361100 , China
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7
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Attack-Resistant and Efficient Cancelable Codeword Generation Using Random walk-Based Methods. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-021-06133-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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8
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Zhang Y, Yang J, Huang M, Liu H. Neighborhood component analysis for modeling papermaking wastewater treatment processes. Bioprocess Biosyst Eng 2021; 44:2345-2359. [PMID: 34226973 DOI: 10.1007/s00449-021-02608-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/25/2021] [Indexed: 11/25/2022]
Abstract
It is of great importance to obtain accurate effluent quality indices in time for pulping and papermaking wastewater treatment processes. However, considering the complex characteristics of industrial wastewater treatment systems, conventional modeling methods such as partial least squares (PLS) and artificial neural networks (ANN) cannot achieve satisfactory prediction accuracy. As a supervised metric learning method, neighborhood component analysis (NCA) is able to significantly improve the prediction performance by training an appropriate model in metric space using the distance between samples for papermaking wastewater treatment processes. The results on two data sets show that NCA has a higher prediction accuracy compared with PLS and ANN. Specifically, NCA has the highest determination coefficient (R2) and the lowest root mean square error in a benchmark simulation data set. On the other hand, the results on the data from an industrial wastewater process indicate that NCA has better modeling accuracy and its R2 increases by 32.80% and 29.08% compared with PLS and ANN, respectively. NCA provides a feasible way to realize online monitoring and automatic control in wastewater treatment processes.
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Affiliation(s)
- Yuchen Zhang
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China
| | - Jie Yang
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China
| | - Mingzhi Huang
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, SCNU Environmental Research Institute, South China Normal University, Guangzhou, 510006, China
| | - Hongbin Liu
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China.
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9
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Tuncer T, Dogan S, Ertam F, Subasi A. A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals. Cogn Neurodyn 2021; 15:223-237. [PMID: 33854641 PMCID: PMC7969686 DOI: 10.1007/s11571-020-09601-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/10/2020] [Accepted: 05/14/2020] [Indexed: 12/24/2022] Open
Abstract
Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Abdulhamit Subasi
- College of Engineering, Department of Computer Science, Effat University, Jeddah, 21478 Saudi Arabia
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10
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A novel statistical decimal pattern-based surface electromyogram signal classification method using tunable q-factor wavelet transform. Soft comput 2021. [DOI: 10.1007/s00500-020-05205-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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11
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12
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Tuncer T, Dogan S, Ertam F, Subasi A. A novel ensemble local graph structure based feature extraction network for EEG signal analysis. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102006] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Romero-Laiseca MA, Delisle-Rodriguez D, Cardoso V, Gurve D, Loterio F, Posses Nascimento JH, Krishnan S, Frizera-Neto A, Bastos-Filho T. A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2020; 28:988-996. [PMID: 32078552 DOI: 10.1109/tnsre.2020.2974056] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A low-cost Brain-Machine Interface (BMI) based on electroencephalography for lower-limb motor recovery of post-stroke patients is proposed here, which provides passive pedaling as feedback, when patients trigger a Mini-Motorized Exercise Bike (MMEB) by executing pedaling motor imagery (MI). This system was validated in an On-line phase by eight healthy subjects and two post-stroke patients, which felt a closed-loop commanding the MMEB due to the fast response of our BMI. It was developed using methods of low-computational cost, such as Riemannian geometry for feature extraction, Pair-Wise Feature Proximity (PWFP) for feature selection, and Linear Discriminant Analysis (LDA) for pedaling imagery recognition. The On-line phase was composed of two sessions, where each participant completed a total of 12 trials per session executing pedaling MI for triggering the MMEB. As a result, the MMEB was successfully triggered by healthy subjects for almost all trials (ACC up to 100%), while the two post-stroke patients, PS1 and PS2, achieved their best performance (ACC of 41.67% and 91.67%, respectively) in Session #2. These patients improved their latency (2.03 ± 0.42 s and 1.99 ± 0.35 s, respectively) when triggering the MMEB, and their performance suggests the hypothesis that our system may be used with chronic stroke patients for lower-limb recovery, providing neural relearning and enhancing neuroplasticity.
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14
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Aydemir E, Tuncer T, Dogan S. A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Med Hypotheses 2019; 134:109519. [PMID: 31877443 DOI: 10.1016/j.mehy.2019.109519] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/28/2019] [Accepted: 12/07/2019] [Indexed: 11/24/2022]
Abstract
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation.
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Affiliation(s)
- Emrah Aydemir
- Department of Computer Engineering, Engineering Faculty, Kirsehir Ahi Evran University, Kirsehir, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
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15
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A novel local senary pattern based epilepsy diagnosis system using EEG signals. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:939-948. [PMID: 31482442 DOI: 10.1007/s13246-019-00794-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
Abstract
Epilepsy is a critical and widely seen neurological disorder for people and electroencephalogram (EEG) signals are used to diagnose epilepsy. To accurately diagnose epilepsy, distinctive features of the EEG signals should be extracted. Therefore, a novel texture descriptor is presented for distinctive feature extraction in this study and an EEG recognition method is proposed. The proposed method consists of four main phases. These are feature extraction, feature concatenation, feature reduction and classification. Firstly, the EEG signal is divided into 1 × 25 size of overlapping blocks and these blocks are converted to 2 dimensional blocks with size of 5 × 5. Because, the proposed novel local senary pattern (LSP) uses 5 × 5 size of blocks for feature extraction. 1536 Features are extracted using the proposed LSP. The proposed LSP is used ternary function to extract features and as we know that the main problem of the ternary function is to find optimal threshold value. Therefore, we used 10 threshold values by using standard deviation function and 1536 × 10 = 15,360 features are extracted from an EEG signal. In the feature combining phase, these features are concatenated. In order to reduce these features, a neighborhood component analysis based feature reduction method is used. In the classification phase support vector machine, k nearest neighborhood, quadratic discriminant analysis and linear discriminant analysis are utilized as the classifiers. To test success of the proposed method, the widely used EEG signals dataset which is Bonn University EEG database is used and 7 cases are defined for testing using this database and the proposed method achieved 93.0% classification accuracy for 5 classes case. The obtained results and comparisons clearly indicated success of the proposed LSP based method.
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16
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Delisle-Rodriguez D, Cardoso V, Gurve D, Loterio F, Alejandra Romero-Laiseca M, Krishnan S, Bastos-Filho T. System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation. J Neural Eng 2019; 16:056005. [DOI: 10.1088/1741-2552/ab08c8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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17
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18
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Zheng J, Qiu H, Wang W, Kong C, Wang H. Kernel-based discriminative elastic embedding algorithm. APPL INTELL 2015. [DOI: 10.1007/s10489-015-0709-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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19
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A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.07.057] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Wang F, Zhang H, Wang K, Zuo W. Fast neighbourhood component analysis with spatially smooth regulariser for robust noisy face recognition. IET BIOMETRICS 2014. [DOI: 10.1049/iet-bmt.2013.0087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Faqiang Wang
- Computational Perception and Cognition Centre, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbin150001People's Republic of China
| | - Hongzhi Zhang
- Computational Perception and Cognition Centre, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbin150001People's Republic of China
| | - Kuanquan Wang
- Computational Perception and Cognition Centre, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbin150001People's Republic of China
| | - Wangmeng Zuo
- Computational Perception and Cognition Centre, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbin150001People's Republic of China
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