A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture
This addresses the black box problem in high-risk medical decision-making, offering a transparent system for sleep stage scoring, though it is incremental as it builds on existing deep learning methods with added interpretability.
The study tackled the lack of interpretability in deep learning for medical applications by designing an interpretable deep neural network for sleep stage scoring using EEG signals, achieving greater performance than prior studies and showing consistency with expert knowledge.
In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer based on a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from the microstructure of EEG signals, such as trained kernels and the effect of each kernel on the detected stages, to macrostructures, such as the transition between stages. The proposed system demonstrated greater performance than prior studies and the results of interpretation showed that the system learned information which was consistent with expert knowledge.