DoC-Informer: Automated Discrimination of Disorders of Consciousness under Adaptive EEG Settings.

Abstract

Disorders of consciousness (DoC) diagnosis critically depends on accurate state discrimination to guide treatment and prognosis. Current EEG-based techniques face challenges of incomplete electrode coverage and manual feature reliance, due to the complex nature of DoC conditions. This study proposes DoC-Informer, a CNN-Transformer framework for automated DoC discrimination under adaptive EEG settings. By integrating a channel-independent architecture-enabled by electrode position encoding and spatial transformers-with channel masking training, the framework employs: 1) Shallow Temporal Feature Encoding with parallel temporal convolutions to extract channel-independent temporal features; 2) Spatiotemporal Representation Modeling using a Spatial Transformer (with 3D electrode position encoding) to infer spatial dependencies and a Temporal Transformer for long-range dynamics. A Channel Masking Training Strategy enhances robustness to incomplete data. Extensive experiments on two real-world DoC datasets (including UWS and MCS patients) demonstrate DoC-Informer's superiority over the cutting-edge deep learning counterparts and a machine learning baseline, with results showing: 1) State-of-the-art performance, 2) Robustness to channel loss, and 3) Validated module efficacy via ablation studies. DoC-Informer bridges brain science and clinical needs by integrating anatomical priors (electrode coordinates) with adaptive deep learning. Its resilience to variable EEG configurations offers a practical solution for real-world DoC diagnosis, particularly in settings with sparse or incomplete recordings. The source code of the framework is available at https://github.com/pilonglin/docinformer.