Metadata Supported Multi-Variate Multi-Scale Attention for Onset Detection and Prediction

Multivariate timeseries data are highly common in the healthcare domain, especially in the neuroscience field for detecting and predicting seizures to monitoring intracranial hypertension (ICH). Unfortunately, conventional techniques to leverage the available time series data do not provide high degrees of accuracy. To address this challenge, the dissertation focuses on onset prediction models for children with brain trauma in collaboration with neurologists at Phoenix Children’s Hospital. The dissertation builds on the key hypothesis that leveraging spatial information underlying the electroencephalogram (EEG) sensor graphs can significantly boost the accuracy in a multi-modal environment, integrating EEG with intracranial pressure (ICP), arterial blood pressure (ABP) and electrocardiogram (ECG) modalities. Based on this key hypothesis, the dissertation focuses on novel metadata supported multi-variate time series analysis algorithms for onset detection and prediction. In particular, the dissertation investigates a model architecture with a dual attention mechanism to draw global dependencies between inputs and outputs, leveraging self-attention in EEG data using multi-head attention for transformers, and long short-term memory (LSTM). However, recognizing that the positional encoding used traditionally in transformers does not help capture the spatial/neighborhood context of EEG sensors, the dissertation investigates novel attention techniques for performing explicit spatial learning using a coupled model network. This dissertation has answered the question of leveraging transformers and LSTM to perform implicit and explicit learning using a metadata supported coupled model network a) Robust Multi-variate Temporal Features (RMT) model and LSTM, b) the convolutional neural network - scale space attention (CNN-SSA) and LSTM mapped together using Multi-Head Attention with explicit spatial metadata for EEG sensor graphs for seizure and ICH onset prediction respectively. In addition, this dissertation focuses on transfer learning between multiple groups where target patients have lesser number of EEG channels than the source patients. This incomplete data poses problems during pre-processing. Two approaches are explored using all predictors approach considering spatial context to guide the variates who are used as predictors for the missing EEG channels, and common core/subset of EEG channels. Under data imputation K-Nearest Neighbors (KNN) regression and multi-variate multi-scale neural network (M2NN) are implemented, to address the problem for target patients.