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Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.<\/jats:p>","DOI":"10.3390\/s18051372","type":"journal-article","created":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T03:45:49Z","timestamp":1525059949000},"page":"1372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals"],"prefix":"10.3390","volume":"18","author":[{"given":"Yinda","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yexian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingfeng","family":"Han","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8108-6007","authenticated-orcid":false,"given":"Fengfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1093\/brain\/aww091","article-title":"Seizure prediction: Making mileage on the long and winding road","volume":"139","author":"Mormann","year":"2016","journal-title":"Brain"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1109\/TBME.2010.2046417","article-title":"Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform","volume":"57","author":"Zandi","year":"2010","journal-title":"IEEE Trans. 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