2022-12-31-farahat22a.md

abstract	booktitle	title	volume	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
We propose a general framework for diagnosing brain disorders from Electroencephalography (EEG) recordings, in which a generative model is trained with EEG data from normal healthy brain states to subsequently detect any systematic deviations from these signals. We apply this framework to the early diagnosis of latent epileptogenesis prior to the first spontaneous seizure. We formulate the early diagnosis problem as an unsupervised anomaly detection task. We first train an adversarial autoencoder to learn a low-dimensional representation of normal EEG data with an imposed prior distribution. We then define an anomaly score based on the number of one-second data samples within one hour of recording whose reconstruction error and the distance of their latent representation to the origin of the imposed prior distribution exceed a certain threshold. Our results show that in a rodent epilepsy model, the average reconstruction error increases as a function of time after the induced brain injury until the occurrence of the first spontaneous seizure. This hints at a protracted epileptogenic process that gradually changes the features of the EEG signals over the course of several weeks. Overall, we demonstrate that unsupervised learning methods can be used to automatically detect systematic drifts in brain activity patterns occurring over long time periods. The approach may be adapted to the early diagnosis of other neurological or psychiatric disorders, opening the door for timely interventions.	Proceedings of the 7th Machine Learning for Healthcare Conference	Diagnosing Epileptogenesis with Deep Anomaly Detection	182	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	farahat22a	0	Diagnosing Epileptogenesis with Deep Anomaly Detection	325	342	325-342	325	false	Farahat, Amr and Lu, Diyuan and Bauer, Sebastian and Neubert, Valentin and Costard, Lara Sophie and Rosenow, Felix and Triesch, Jochen	given family Amr Farahat given family Diyuan Lu given family Sebastian Bauer given family Valentin Neubert given family Lara Sophie Costard given family Felix Rosenow given family Jochen Triesch	2022-12-31		Proceedings of the 7th Machine Learning for Healthcare Conference	inproceedings	date-parts 2022 12 31	https://proceedings.mlr.press/v182/farahat22a/farahat22a.pdf