Epileptic seizure detection is a challenging research topic. The objective of this research is to analyze the performance of various classification techniques while detecting the epileptic seizure in a shorter time. In this paper, we apply four different types of classifiers-two are black-box (SVM & KNN) and other two are non-black-box (Decision tree & Ensemble) on two epileptic patient seizure data sets. Our finding shows that non-black box classifiers, specifically ensemble classifiers, do better than other classifiers. The experimental results indicate that the ensemble classifier can assist for seizure detection in a shorter epoch length of time (i.e., 0.5 s) with high accuracy rate. Significantly in comparison to other classifiers the ensemble classifier provides high accuracy and less chance of false detection rate.
|Title of host publication||Advanced data mining and applications|
|Subtitle of host publication||13th international conference, ADMA 2017, proceedings|
|Editors||Gao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun|
|Place of Publication||Cham, Switzerland|
|Number of pages||13|
|Publication status||Published - 2017|
|Event||The 13th International Conference on Advanced Data Mining and Applications (ADMA): ADMA 2017 - Nanyang Technological University Alumni House, Singapore, Singapore|
Duration: 05 Nov 2017 → 06 Nov 2017
http://www.adma2017.net/#0 (Conference website)
|Name||Lecture Notes in Artificial Intelligence|
|Conference||The 13th International Conference on Advanced Data Mining and Applications (ADMA)|
|Period||05/11/17 → 06/11/17|
|Other||The year 2017 marks the 13th anniversary of the International Conference on Advanced Data Mining and Applications (ADMA 2017), which will be held in Singapore, 5—6 Nov 2016, co-located with ACM CIKM2017. It is our great pleasure to invite you to contribute papers and participate in this premier annual event on research and applications of data mining.|
A growing attention has been paid to the study, development, and application of data mining. As a result, there is an urgent need for sophisticated techniques and tools that can handle new subfields of data mining, e.g., smartphone and social network data mining, spatial data mining, streaming data mining, green computing data mining, biomedical data mining, the Internet of Things, and data mining for healthcare. Our expertise in data mining also has to be expanded to new applications.