A method and system for processing outliers in electromyographic data

Abstract

The invention relates to a method and a system for processing outliers in electromyographic data. The method comprises the following steps: acquiring a data set of a data sample, and dividing the dataset into a first type positive domain, a first type boundary region, a second type positive domain and a second type boundary region; clustering the data samples of the first and second boundary regions, and dividing the first and second boundary regions into a plurality of groups according to the clustering results; according to the distance from the data sample in the first boundary region to the first positive region and the distance from the data sample in the second boundary region to the second positive region, determining an average distance from each group of the first type boundary region to the first type positive domain and an average distance from each group of the second type boundary region to the second type positive domain, marking a group whose average distance is greaterthan a distance threshold as an outlier, and taking a set outlier as an outlier; marking the outliers whose data sample size is larger than the enclave threshold as enclave groups, and deleting the enclave groups in the outlier set to obtain the outlier processing results.

Description

Technical field [0001] The invention belongs to the field of machine learning, and in particular relates to a method and system for processing outliers in electromyographic data, which is applicable to the scene of using electromyographic signals to detect abnormal muscles. Background technique [0002] When using EMG signals for abnormal muscle detection, a large number of EMG samples with labels (that is, abnormal muscles and normal muscles) are often required, and an abnormal muscle detection model is trained based on this data set to achieve the purpose of identifying abnormal muscles . It is hoped that the abnormal muscle recognition model can achieve a good generalization effect on the real data set in the future, but the generalization ability of the model is closely related to the quality of the data set. Due to various external influences in the process of EMG signal acquisition, such as motion interference, electromagnetic interference, power frequency noise, circ...

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Problems solved by technology

[0004] In order to solve the problem of the influence of outliers in the data set on the generalization ability of the model in the process of establishing the abnormal muscle recognition model, the present invention mainly solves the problem that the existing outlier point recognition method misidentifies the outliers and non-noise groups caused by insufficient samples in the data set The problem that the group is mistakenly identified as an outlier and deleted, and it also solves the problem that the existing method cannot identify all the outlier sample groups with noise patterns

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