Artificial intelligence self-learning-based static electrocardiography analysis method and apparatus

An artificial intelligence self-learning-based resting electrocardiogram analysis method, comprising:

• receiving resting electrocardiogram data output by a resting electrocardiogram monitoring device, and converting a data format of the resting ECG data into a preset standard data format by resampling, and performing a first filtering processing on converted resting ECG data in the preset standard data format;
• performing heart beat detection processing on resting ECG data processed by the first filtering processing to identify multiple pieces of heart beat data comprised in the resting ECG data, each of which is corresponding to a heart beat cycle, comprising amplitude data and starting-ending time data of corresponding P wave, QRS complex and T wave;
• and lead parameters of the heart beat data are determined;
• combining the heart beat data to generate heart beat time sequence data based on the lead parameters and time rules;
• and generating heart beat analysis data according to the heart beat time sequence data;
• performing feature extraction and analysis of an amplitude and time characterization data on the heart beat analysis data according to a trained heart beat classification model, to obtain primary classification information of the heart beat analysis data;
• inputting the heart beat analysis data of particular heart beats in results of the primary classification information into a trained ST segment and T wave change model for identification, and determining ST segment and T wave evaluation information;
• performing secondary classification processing on the heart beat analysis data according to resting ECG basic rule reference data, detailed feature information of the P wave and the T wave and the ST segment and T wave evaluation information under the primary classification information to finally obtain heart beat classification information;
• according to the heart beat time sequence data and the heart beat classification information, performing heart beat waveform feature detection on the heart beat analysis data to determine feature heart beats;
• determining detailed feature information of the QRS complex in the feature heart beats;
• determining the detailed feature information of the P wave and the T wave in the feature heart beats;
• and calculating average waveform data and measurement values of the feature heart beats;
• and
• for data of the heart beat classification information and the average waveform data of the feature heart beats, generating and outputting ECG event data, data of the measurement values and the average waveform data of the feature heart beats according to the resting ECG basic rule reference data and a classification standard of ECG Minnesota coding.