RT Journal Article SR Electronic T1 Deep learning using EEG spectrograms for prognosis in idiopathic rapid eye movement behavior disorder (RBD) JF bioRxiv FD Cold Spring Harbor Laboratory SP 240267 DO 10.1101/240267 A1 Giulio Ruffini A1 David Ibañez A1 Eleni Kroupi A1 Jean-François Gagnon A1 Jacques Montplaisir A1 Marta Castellano A1 Aureli Soria-Frisch YR 2018 UL http://biorxiv.org/content/early/2018/01/29/240267.abstract AB REM Behavior Disorder (RBD) is a serious risk factor for neurodegenerative diseases such as Parkinson’s disease (PD). In this paper we describe deep learning methods for RBD prognosis classification from electroencephalography (EEG). We work using a few minutes of eyes-closed resting state EEG data collected from idiopathic RBD patients (121) and healthy controls (HC, 91). At follow-up after the EEG acquisition (mean of 4 ±2 years), a subset of the RBD patients eventually developed either PD (19) or Dementia with Lewy bodies (DLB, 12), while the rest remained idiopathic RBD. We describe first a deep convolutional neural network (DCNN) trained with stacked multi-channel spectrograms, treating the data as in audio or image problems where deep classifiers have proven highly successful exploiting compositional and translationally invariant features in the data. Using a multi-layer architecture combining filtering and pooling, the performance of a small DCNN network typically reaches 80% classification accuracy. In particular, in the HC vs PD-outcome problem using a single channel, we can obtain an area under the curve (AUC) of 87%. The trained classifier can also be used to generate synthetic spectrograms to study what aspects of the spectrogram are relevant to classification, highlighting the presence of theta bursts and a decrease of power in the alpha band in future PD or DLB patients. For comparison, we study a deep recurrent neural network using stacked long-short term memory network (LSTM) cells [1, 2] or gated-recurrent unit (GRU) cells [3], with similar results. We conclude that, despite the limitations in scope of this first study, deep classifiers may provide a key technology to analyze the EEG dynamics from relatively small datasets and deliver new biomarkers.