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

TL;DR

This paper introduces EasiCSDeep, a novel deep learning model that uses surface electromyography signals for early, low-cost, and accurate identification of cervical spondylosis, addressing diagnostic challenges.

Contribution

The paper presents the first deep learning approach utilizing sEMG data for cervical spondylosis detection, improving accuracy over existing methods.

Findings

Significant accuracy improvement over previous algorithms

Effective handling of high-dimensional sEMG data

First application of deep learning for CS identification using sEMG

Abstract

Cervical spondylosis (CS) is a common chronic disease that affects up to two-thirds of the population and poses a serious burden on individuals and society. The early identification has significant value in improving cure rate and reducing costs. However, the pathology is complex, and the mild symptoms increase the difficulty of the diagnosis, especially in the early stage. Besides, the time-consuming and costliness of hospital medical service reduces the attention to the CS identification. Thus, a convenient, low-cost intelligent CS identification method is imperious demanded. In this paper, we present an intelligent method based on the deep learning to identify CS, using the surface electromyography (sEMG) signal. Faced with the complex, high dimensionality and weak usability of the sEMG signal, we proposed and developed a multi-channel EasiCSDeep algorithm based on the convolutional neural network, which consists of the feature extraction, spatial relationship representation and classification algorithm. To the best of our knowledge, this EasiCSDeep is the 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 a significant improvement.