Granted Patent B2

US 12,616,422 · App. 17/312,946 · Granted May 5, 2026

Inventors: Zedong Nie (Shenzhen, CN); Jingzhen Li (Shenzhen, CN); Yuhang Liu (Shenzhen, CN)

Assignee: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

A61B5/7275A61B5/33A61B5/374A61B5/6803A61B5/7246A61B5/7264G16H50/20G16H50/70

Abstract

Claims (22)

1 . A non-invasive prediabetes detection method based on combination of electrocardiogram and electroencephalogram information, comprising steps of:

S 1 , during an oral glucose tolerance test, synchronously obtaining an electrocardiogram (ECG) signal and an electroencephalogram (EEG) signal of a user in a noninvasive manner by utilizing a wearable device;

S 2 , performing dimension reduction processing on a combined feature set composed of ECG features and EEG features in various ways to obtain a plurality of dimension-reduced combined feature sets, and select the ECG features and the EEG features meeting a preset criteria of correlation by analyzing a correlation between the plurality of dimension-reduced combined feature sets and a blood glucose concentration value to constitute an optimized combined feature set; and

S 3 , respectively inputting the optimized combined feature set into a plurality of trained neural network models, obtaining from each trained neural network model a classification output that classifies the user as prediabetic or non-prediabetic, and determining that the user is prediabetic when a majority of the classification outputs classify the user as prediabetic, and

Outputting, via voice or text, a determination indicating whether the user is prediabetic or non-prediabetic;

wherein S 1 comprise substeps of:

placing six ECG electrodes V 1 to V 6 configured to monitor the ECG signal onto the user's chest, wherein the ECG electrode V 1 is placed in a fourth intercostal space at a right border of a sternum, the ECG electrode V 2 is placed in a fourth intercostal space at a left border of the sternum, and the ECG electrode V 3 is placed in a midpoint of a connecting line between the ECG electrode V 2 and the ECG electrode V 4 , the ECG electrode V 4 is placed at an intersection between a left mid-clavicular line and a fifth intercostal space, the ECG electrode V 5 is parallel to an anterior axillary line, and the ECG electrode V 6 is parallel to a midaxillary line;

wearing an EEG electrode cap on the user's head, six EEG electrodes configured to monitor the EEG signal being provided in the EEG electrode cap, wherein the six EEG electrodes are respectively corresponding to a frontal lobe, an occipital lobe and a parietal lobe of a left hemisphere of a brain, and a frontal lobe, an occipital lobe and a parietal lobe of a right hemisphere of the brain; and

carrying out the oral glucose tolerance test, and starting an ECG collection device and an EEG collection device to synchronously obtain the ECG signal and the EEG signal of the user:

wherein S 2 comprising substeps of:

extracting, from the ECG signal, feature information of a plurality of different segments, and respectively extract, from the EEG signal, EEG feature information of different frequency bands corresponding to different positions of a brain, to constitute the combined feature set;

performing dimension reduction processing on the combined feature set based on a principal component analysis to obtain a first combined feature set;

performing dimension reduction processing on the combined feature set based on an independent component analysis to obtain a second combined feature set;

performing dimension reduction processing on the combined feature set based on a lasso regression analysis to obtain a third combined feature set; and

analyzing the correlation between the blood glucose concentration and the first combined feature set, the second combined feature set, and the third combined feature set respectively, and then screen out the ECG features and the EEG features in the first combined feature set, the second combined feature set, and the third combined feature set that meet the preset criteria of correlation, to constitute the optimized combined feature set.

2 . The non-invasive prediabetes detection method based on combination of electrocardiogram and electroencephalogram information according to claim 1 , wherein the correlation is analyzed based on a Pearson correlation analysis, and the preset criteria of correlation is set as correlation k>0.2 and P≤0.05, P representing a probability of performing hypothesis testing on a correlation coefficient.

3 . The non-invasive prediabetes detection method based on combination of electrocardiogram and electroencephalogram information according to claim 1 , wherein the performing dimension reduction processing on the combined feature set based on the principal component analysis comprises:

calculating a covariance matrix of a feature point of each feature in the combined feature set; and

calculating eigenvectors of the covariance matrix and eigenvalues corresponding to the eigenvectors:

sorting the eigenvectors according to magnitudes of the eigenvalues to form a matrix u=[u 1 , u 2 , u 3 , . . . , u n ], the corresponding eigenvalues being λ 1 , λ 2 , λ 3 , . . . , λ n in descending order, and intercepting, from the matrix u, a certain proportion of top-ranked eigenvalues as new feature points of each feature to achieve data dimension reduction.

4 . The non-invasive prediabetes detection method based on combination of electrocardiogram and electroencephalogram information according to claim 1 , wherein the plurality of types of trained neural network models comprise at least two types of a convolutional neural network, a long-short term memory network, and a recurrent neural network.

5 . The non-invasive prediabetes detection method based on combination of electrocardiogram and electroencephalogram information according to claim 1 , wherein the determination that the user is prediabetic, further classifies the user as suitable for intervention measures to reduce the risk of progression to diabetes.

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (13)