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Cervical spondylosis (CS) is a common chronic disease that affects up totwo-thirds of the population and poses a serious burden on individuals andsociety. The early identification has significant value in improving cure rateand reducing costs. However, the pathology is complex, and the mild symptomsincrease the difficulty of the diagnosis, especially in the early stage.Besides, the time-consuming and costliness of hospital medical service reducesthe attention to the CS identification. Thus, a convenient, low-costintelligent CS identification method is imperious demanded. In this paper, wepresent an intelligent method based on the deep learning to identify CS, usingthe surface electromyography (sEMG) signal. Faced with the complex, highdimensionality and weak usability of the sEMG signal, we proposed and developeda multi-channel EasiCSDeep algorithm based on the convolutional neural network,which consists of the feature extraction, spatial relationship representationand classification algorithm. To the best of our knowledge, this EasiCSDeep isthe first effort to employ the deep learning and the sEMG data to identify CS.Compared with previous state-of-the-art algorithm, our algorithm achieves asignificant improvement.

EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signal