EEG Temporal-Spatial Feature Learning for Automated Selection of Stimulus Parameters in Electroconvulsive Therapy.

The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be high if an excessive stimulus is applied to induce the necessary generalized seizure (GS); Conversely, inadequate stimulus results in failure. Recent efforts to automate this task can facilitate statistical analyses on individual parameters or qualitative predictions. However, this automation still significantly lags behind the requirements in clinical practices. This study addresses this issue by predicting the probability of GS induction under the joint restriction of a patient's EEG (electroencephalogram) and the stimulus parameters, sustained by a two-stage learning model (namely ECTnet): 1) Temporal-Spatial Feature Learning. Channel-wise convolution via multiple convolution kernels first learns the deep features of the EEG, followed by a "ConvLSTM" constructing the temporal-spatial features aided with the enforced convolution operations at the LSTM gates; 2) GS Prediction. The probability of seizure induction is predicted based on the EEG features fused with stimulus parameters, through which the optimal parameter setting(s) may be obtained by minimizing the stimulus charge while ensuring the probability above a threshold. Experiments have been conducted on EEG data from 96 subjects with mental disorders to examine the performance and design of ECTnet. These experiments indicate that ECTnet can effectively automate the selection of optimal stimulus parameters: 1) an AUC of 0.746, F1-score of 0.90, a precision of 89% and a recall of 93% in the prediction of seizure induction have been achieved, outperforming the state-of-the-art counterpart, and 2) inclusion of parameter features increases the F1-score by 0.054.