Scatter diagram classification method and apparatus for photoplethysmography signal

A scatter diagram classification device for a photoplethysmography (PPG) signal, characterized in that, the device comprising:

• an acquisition module for acquiring the PPG signal;
• a sampling module for performing signal sampling processing on the PPG signal according to a preset sampling frequency to generate a PPG sampling signal;
• a scatter diagram module for performing scatter point two-dimensional coordinate preparation processing according to the PPG sampling signal to generate a scatter point two-dimensional coordinate sequence;
• performing scatter diagram resolution confirmation processing according to the sampling frequency and a preset maximum value of an inter-beat interval to generate a scatter diagram resolution;
• performing scatter diagram initialization processing according to the scatter diagram resolution to generate a scatter diagram two-dimensional tensor;
• and performing scatter point marking processing on the scatter diagram two-dimensional tensor according to the scatter point two-dimensional coordinate sequence;
• an artificial intelligence calculation module for using a convolutional neural network of an artificial intelligence network to perform multi-layer convolution pooling calculation on the scatter diagram two-dimensional tensor to generate a four-dimensional output tensor;
• using a fully connected neural network of the artificial intelligence network to perform multi-layer full connection calculation on the four-dimensional output tensor to generate a two-dimensional output tensor;
• and using a normalization processing layer of the artificial intelligence network to perform normalization index calculation on the two-dimensional output tensor to generate a normalization two-dimensional tensor, the artificial intelligence network comprising the convolutional neural network, the fully connected neural network and the normalization processing layer;
• and
• a classification confirmation module for performing classification confirmation processing according to the normalization two-dimensional tensor to generate confirmation data, the confirmation data comprising classification reasonable information and classification unreasonable information.