Parkinson's disease patient dyskinesia quantification and identification method based on support vector machine

Abstract

The invention belongs to the technical field of signal identification, particularly relates to a Parkinson's disease patient dyskinesia quantification and identification method based on a support vector machine, and aims to solve the problem that Parkinson's disease patient dyskinesia quantification and identification cannot be accurately realized in the prior art. The method comprises the following steps: acquiring wrist motion signals and ankle motion signals of a tested object and a healthy person; carrying out resampling and signal synthesis on the signals; extracting a walking interval through a sliding window; extracting the gait features of the measured object and the healthy person respectively; normalizing the gait features and classifying the gait features through a trained support vector machine; calculating Pr values, wherein the intervals Pr is greater than 0.9, Pr is less than 0.9 and greater than or equal to 0.6, Pr is less than 0.6 and greater than or equal to 0.5 and Pr is less than 0.5, which corresponds to a severe Parkinson's disease patient, a moderate Parkinson's disease patient, a mild Parkinson's disease patient and a non-Parkinson's disease patient respectively. The Parkinson's disease patient dyskinesia quantification and identification method is high in accuracy, high in precision, small in occupied resource, suitable for remote medical treatment, lowin cost and high in efficiency.

Description

technical field [0001] The invention belongs to the technical field of signal identification, and in particular relates to a method for quantifying and identifying movement disorders of Parkinson's patients based on a support vector machine. Background technique [0002] Parkinson's disease is a chronic neurological disorder characterized by bradykinesia, tremor, stiffness, and abnormal posture and gait. At present, there are more than 6 million Parkinson's patients in the world, most of them are elderly people, and they need to be tested much longer than young people. It has been found in some medical research that extrapyramidal diseases represented by Parkinson's disease have the characteristics of high morbidity, high disability rate, and complex and diverse forms of movement disorders. In clinical work, clinicians mainly rely on clinical experience to determine the severity and judgment of Parkinson's patients. However, because clinical experience is too subjective, it...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above-mentioned problems in the prior art, that is, the problem that the prior art cannot accurately realize the quantification and identification of Parkinson's patients' dyskinesias, the present invention provides a method for quantifying and identifying Parkinson's patients' dyskinesias based on a support vector machine. Methods include:

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