Recognition of Sleep-Wake Stages by Deep Takagi-Sugeno-Kang Fuzzy Classifier with Random Rule Heritage

Due to the existence of noise and/or uncertainty in available sleep-wake stage data, recognition of sleep-wake stages with an efficient feature extraction has been facing the following challenges: (1) how to handle these uncertainties; (2) how to have interpretable recognition; (3) how to make recognition of sleep-wake stages to have good testing performance (i.e., generalization capability). To circumvent these three challenges, a deep Takagi-Sugeno-Kang (TSK) fuzzy classifier with random rule heritage called Drrh-TSK-FC is proposed for recognition of sleep-wake stages by integrating enhanced generalization capability of deep structure in fully excavating sleep information hidden in sleep stage data, together with the excellence of fuzzy classifiers in handling uncertainty and providing interpretability in this study. Drrh-TSK-FC assures both enhanced classification performance and strong interpretability by stacking the original input space plus the randomly projected outputs of the previous TSK fuzzy sub-classifier into current sub-classifier in a deep learning way. Besides, it has three distinctive characteristics: (1) the cognitive behavior is mimicked that people often leverage the past experiences (i.e., rules) to solve new yet similar problems. (2) in order to tackle with the changing importance of features caused by the randomness in the generation of the input space for each TSK fuzzy sub-classifier, some randomly generated short fuzzy rules are used. (3) a linear combination of all TSK fuzzy sub-classifiers is taken to facilitate with possibly different importance of the features in each sub-classifier. Moreover, Drrh-TSK-FC can be quickly trained by our previous work—least learning machine (LLM) or its variant in this study. The experimental results on recognition of sleep-wake stage tasks indicate that except for strong interpretability, the proposed Drrh-TSK-FC still keeps at least comparable classification performance to the adopted comparative methods.