Heart rate variability characteristic analysis method based on deep learning

The invention discloses a heart rate variability characteristic analysis method based on deep learning, and relates to the field of computers. Information contained in heart rate variability can be effectively extracted. The method comprises the following steps: acquiring electrocardiogram data through a sensor, and transmitting the electrocardiogram data to an upper computer; preprocessing the ECG signal by applying interpolation and filtering; intercepting the received ECG data by adopting a sliding window, and performing real-time updating and Fourier transform to obtain a frequency spectrum feature map; and analyzing the spectrogram and extracting useful information by using a deep convolutional neural network, including frequency domain feature extraction based on a Resnet18 residual learning method and time-frequency domain feature fusion based on an attention mechanism, and classifying and outputting feature information by using a multi-layer perceptron. 一种基于深度学习的心率变异性特征分析方法涉及计算机领域，能够有效提取心率变异性中包含的信息。本发