A community effort for automatic detection of postictal generalized EEG suppression in epilepsy

Abstract

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

Keywords: Detection; Electroencephalographic; Intermittent slow wave; Physiological signal; Postictal Generalized electroencephalographic suppression; Prediction.

Fig. 1

End of PGES and onset of first intermittent slow activity. Our objective is to detect the transition of PGES to the slow activity during the latency period. GTC generalized tonic-clonic, PGES postictal generalized electroencephalographic suppression

Fig. 2

Data augmentation. If the crop reaches the end of PGES, then the crop was set as positive (i.e., label = 1), otherwise negative (i.e., label = 0)

Fig. 3

CNN-based classifier for real-time suppression detection. Input were raw EEG segments cropped from sliding windows during PGES and latency periods. Output was the probability that PGES ends

Fig. 4

Comparing raw EEG signals and the estimated probability of slow activity. The green area refers to slow activity. a True positive detection. b False positive or false negative detection