{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:00:25Z","timestamp":1789048825947,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T00:00:00Z","timestamp":1666483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer\u2019s. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer\u2019s and other neurodegenerative diseases later in life. Utilizing recordings of patients\u2019 brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients\u2019 brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients\u2019 brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks.<\/jats:p>","DOI":"10.3390\/s22218112","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Brain Age Prediction\/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects"],"prefix":"10.3390","volume":"22","author":[{"given":"Kameron","family":"Jusseaume","sequence":"first","affiliation":[{"name":"Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6550-2533","authenticated-orcid":false,"given":"Iren","family":"Valova","sequence":"additional","affiliation":[{"name":"Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neucom.2015.04.025","article-title":"Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics","volume":"166","author":"Armstrong","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.3389\/fnins.2019.01125","article-title":"Generating brain waves, the power of astrocytes","volume":"13","author":"Buskila","year":"2019","journal-title":"Front. 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