Detecting Disorders of Consciousness in Brain Injuries From EEG Connectivity Through Machine Learning

Abstract

Disorders of consciousness (DoC) happen frequently in various brain injuries. Their detection helps timely treatment for better survival outcomes of DoC patients. It is conventionally undertaken via clinical examinations, typically behavioural assessments. However, these neurological examinations consume significant resources of manpower and time, making continuous DoC monitoring practically infeasible. To address this issue, a computer-aided approach is proposed in this article for automated DoC detection through extracting knowledge from electroencephalogram (EEG) signals. It introduces a new connectivity measure: Power Spectral Density Difference (PSDD) incorporating with a recursive Cosine function (CPSDD). Then, the approach classifies brain-injured patients into DoC (i.e., positive) and wakefulness (i.e., negative) classes through an ensemble of support vector machines (EOSVM), which is a type of machine-learning methods. It is further applied to a dataset of 607 patients with brain injuries. Our classification results show that the EOSVM classifier with the new connectivity measure CPSDD has achieved the best classification performance among 12 connectivity measures. For a setting of 97% majority voting from all SVMs, the EOSVM has diagnosed, in high confidence, 35% of patients with the accuracy, sensitivity, and specificity of 98.21%, 100%, and 95.79%, respectively. Thus, the classifier EOSVM incorporating with the new connectivity measure CPSDD is a promising tool for automatic detection of DoC in brain injuries.