An Interpretable Deep Learning Framework for Health Monitoring Systems: A Case Study of Eye State Detection using EEG Signals

Publication Type:
Conference Proceeding
2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2021, 00, pp. 211-218
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Effective monitoring and early detection of deterioration in patients play an essential role in healthcare. This includes minimizing the number of emergency encounters, reducing the length of hospitalization stay, re-admission rates of the patients, and etc. Cutting-edge methods in artificial intelligence (AI) have the ability to significantly improve outcomes. However, the struggle to interpret these black box models presents a serious problem to the healthcare industry. When selecting a model, the decision to sacrifice accuracy for interpretability must be made. In this paper, we propose an interpretable framework with the ability of real-time prediction. To demonstrate the predictive power of the framework, a case study on eye state detection using electroencephalogram (EEG) signals was employed to investigate how a deep neural network (DNN) model makes a prediction, and how that prediction can be interpreted. The promising results can be used to employ more advanced models in healthcare solutions without any concern of sacrificing the interpretation.
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