Feature extraction for epileptic seizure detection using machine learning
Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming. Aim: An automated approach is required that can assist the neurologists in seizure diagnosis in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2020-01-01
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| Series: | Current Medicine Research and Practice |
| Subjects: | |
| Online Access: | http://www.cmrpjournal.org/article.asp?issn=2352-0817;year=2020;volume=10;issue=6;spage=266;epage=271;aulast=Khati |
| _version_ | 1811180127039520768 |
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| author | Renuka Mohan Khati Rajesh Ingle |
| author_facet | Renuka Mohan Khati Rajesh Ingle |
| author_sort | Renuka Mohan Khati |
| collection | DOAJ |
| description | Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming.
Aim: An automated approach is required that can assist the neurologists in seizure diagnosis in order to minimise the diagnostic time.
Materials and Methods: Electroencephalogram (EEG) signals record the electrical activities of the brain and can be used effectively in seizure diagnosis. We have evaluated our system using Bonn university database. Appropriate feature selection is very important in pattern recognition problems. We used recursive feature elimination (wrapper method), for the selection of best features. In our study, we have used EEG signals to evaluate the performance of seven machine learning algorithms.
Results: We obtained a classification accuracy of 99%, which is a significant improvement over the state-of-the-art methods. The classification performance across 10 folds of cross validation of Logistic regression and Adaboost was the highest with an accuracy of 99%. While using naive bayes and random forest, the Area under the ROC curve value obtained was 1. The sensitivity obtained using naive bayes classifier was 100%.
Conclusion: Our approach requires minimum feature extraction that makes it an efficient approach. Recursive feature elimination was used to select the features which yield good accuracy results. It was observed that logistic regression and Adaboost performed superior with an accuracy of 99%. |
| first_indexed | 2024-04-11T06:45:16Z |
| format | Article |
| id | doaj.art-ee6dc6753d0a4dfca5fba33652ae38a2 |
| institution | Directory Open Access Journal |
| issn | 2352-0817 2352-0825 |
| language | English |
| last_indexed | 2024-04-11T06:45:16Z |
| publishDate | 2020-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Current Medicine Research and Practice |
| spelling | doaj.art-ee6dc6753d0a4dfca5fba33652ae38a22022-12-22T04:39:22ZengWolters Kluwer Medknow PublicationsCurrent Medicine Research and Practice2352-08172352-08252020-01-0110626627110.4103/cmrp.cmrp_52_20Feature extraction for epileptic seizure detection using machine learningRenuka Mohan KhatiRajesh IngleBackground: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming. Aim: An automated approach is required that can assist the neurologists in seizure diagnosis in order to minimise the diagnostic time. Materials and Methods: Electroencephalogram (EEG) signals record the electrical activities of the brain and can be used effectively in seizure diagnosis. We have evaluated our system using Bonn university database. Appropriate feature selection is very important in pattern recognition problems. We used recursive feature elimination (wrapper method), for the selection of best features. In our study, we have used EEG signals to evaluate the performance of seven machine learning algorithms. Results: We obtained a classification accuracy of 99%, which is a significant improvement over the state-of-the-art methods. The classification performance across 10 folds of cross validation of Logistic regression and Adaboost was the highest with an accuracy of 99%. While using naive bayes and random forest, the Area under the ROC curve value obtained was 1. The sensitivity obtained using naive bayes classifier was 100%. Conclusion: Our approach requires minimum feature extraction that makes it an efficient approach. Recursive feature elimination was used to select the features which yield good accuracy results. It was observed that logistic regression and Adaboost performed superior with an accuracy of 99%.http://www.cmrpjournal.org/article.asp?issn=2352-0817;year=2020;volume=10;issue=6;spage=266;epage=271;aulast=Khatiepilepsyfeature extractionfeature selection |
| spellingShingle | Renuka Mohan Khati Rajesh Ingle Feature extraction for epileptic seizure detection using machine learning Current Medicine Research and Practice epilepsy feature extraction feature selection |
| title | Feature extraction for epileptic seizure detection using machine learning |
| title_full | Feature extraction for epileptic seizure detection using machine learning |
| title_fullStr | Feature extraction for epileptic seizure detection using machine learning |
| title_full_unstemmed | Feature extraction for epileptic seizure detection using machine learning |
| title_short | Feature extraction for epileptic seizure detection using machine learning |
| title_sort | feature extraction for epileptic seizure detection using machine learning |
| topic | epilepsy feature extraction feature selection |
| url | http://www.cmrpjournal.org/article.asp?issn=2352-0817;year=2020;volume=10;issue=6;spage=266;epage=271;aulast=Khati |
| work_keys_str_mv | AT renukamohankhati featureextractionforepilepticseizuredetectionusingmachinelearning AT rajeshingle featureextractionforepilepticseizuredetectionusingmachinelearning |