39 results about "ECG feature" patented technology

The present invention discloses at least two electrodes disposed at the body of the ECG measuring device of the present invention. When a user normally uses the ECG measuring device, the two electrodes are just adhered to the hands and the face of the user. The ECG measuring method of the present invention is to measure a first ECG signal from the left hand to the left side of face of the user and a second ECG signal from the right hand to the right side of face of the user. Then a plurality of ECG features are obtained from the first ECG signal, the second ECG signal and the result, as related coefficients, of the interactive computing, as a method of plus and subtract, of the first ECG signal and the second ECG signal. Hence, a biometric authorization process is engaged according to the plurality of ECG features.

The invention discloses a pre-diabetes detection system and method based on the combination of electrocardiogram and electroencephalogram information. The system includes: a signal acquisition module, which uses a wearable device to obtain the user's ECG signal and EEG signal synchronously in a non-invasive manner; Dimensionality reduction processing is performed on multiple dimensionality reduction sets to obtain multiple dimensionality reduction combination feature sets, and by analyzing the correlation between multiple dimensionality reduction combination feature sets and blood glucose concentration values, ECG features and EEG features that meet the set correlation criteria are selected. , to form an optimized combined feature set; the multi-modal fusion module is used to input the optimized combined feature set to the trained neural network models of various types, and obtain the user's diabetes by fusing the output results of various types of neural networks. Preliminary test results. The invention can provide a non-invasive, painless, convenient, comfortable and low-cost pre-diabetes detection solution.

The invention discloses a fatigue detection method based on electroencephalogram and electrocardiogram in which the steering wheel is embedded with an electrocardiogram sensor. Fatigue detection is performed by constructing a product fuzzy convolution network, which specifically includes: S1. Obtaining electrocardiogram data through an electrocardiogram detection chip, and using The electroencephalograph obtains EEG time series data; S2, uses a fuzzy neural network with multilayer feedback to process EEG time series data, and obtains EEG features; S3, builds a deep network based on a one-dimensional convolutional neural network framework The feature extraction network extracts the fatigue features of the ECG data and generates the ECG feature sequence; S4. Design the fusion network, input the ECG feature sequence and the EEG feature at the same time, fuse the two signals together, and give the predicted value; S5 , Use the adaptive moment estimation algorithm to optimize and train the network model. It can reduce noise and improve detection accuracy. The introduction of multilayer reduces the limitation of fuzzy neural network on the feature dimension of input data and improves the accuracy of classification results.

The invention provides an ECG (electrocardiograph) feature extraction method integrating Butterworth filtering and wavelet transformation, and belongs to the technical field of medical computers. A core concept is described as follows: performing de-noising processing on power frequency interference of an original ECG signal; meanwhile, removing baseline drift in a curve fitting manner; respectively positioning a wave Q, a wave R and a wave S with a differential threshold method and a wavelet transformation method after a relatively accurate ECG signal is acquired, comparing the waves Q, R andS with information marked by an expert, and respectively regulating threshold, scale and translation amount parameters to obtain an optimal positioning result. Based on the result, the wave Q, the wave R and the wave S are positioned respectively by adopting the differential threshold method or the wavelet transformation method. According to the extraction method provided by the invention, the more accurate de-noising processing can be performed on the original ECG signal; in addition, the waves Q, R and S can be positioned accurately, therefore, a foundation is laid for a digital analysis onan ECG.