Abstract:Nowadays, the number of non-communicable diseases (NCDs) sufferers has increased dramatically surpassing that of infectious diseases, and NCDs have become the most important factor threatening the human health. This leads to heavy burdens on families and the society. Therefore, it is meaningful to early diagnose NCDs. However, it cannot be accomplished clinically at present, so a novel NCDs diagnosis method is necessary. Most NCDs like cardiovascular diseases, cancers and diabetes, can affect the nervous system and cause changes in nerve electrical signals. The state-of-the-art method for early diagnosing NCDs could be accomplished through neuroelectrophysiological methods. Considering the diversity and complication of neural signals, the widely-applied machine learning methods can be combined together with neuroelectrophysiological approaches to quantify the relationships between NCDs and neural signals. This paper generally and systematically review the neuroelectrophysiology incorporated with machine learning in the diagnosis of non-communicable diseases.
