The invention provides a
diabetic neuropathy dynamic early warning method based on a multi-
modal biological
signal, and the method comprises the steps: integrating a multi-
modal sensor through an intelligent wearable device, and constructing a personalized prediction model in combination with a
deep learning algorithm; through an
artificial intelligence algorithm,
diabetic neuropathy (DN) development risk prediction and unperceptual
hypoglycemia real-time early warning are realized. According to the time-
frequency domain characteristics and clinical data of the multi-mode
signal, the time window of the DN from the progress to the serious stage can be predicted; recognizing an unperceptual
hypoglycemia risk by combining ECG and HR features with a blood glucose
trend prediction model, and triggering grading
intervention measures; based on the individual physiological response mode of the patient, the
algorithm threshold is dynamically optimized, and the accuracy of prediction and early warning is improved. According to the method, HR, BP, ECG and other multi-dimensional signals are integrated, and the prediction precision is improved;
continuous monitoring and instant feedback are supported; the self-
adaptive algorithm adapts to individual differences of patients, and the limitation of a traditional general model is overcome.