This invention belongs to the field of
medical testing and
diagnostic technology, and discloses an AI-based, end-to-end intelligent auxiliary
analysis method for electrocardiogram (ECG) testing and diagnosis, comprising: S1, constructing a three-dimensional mapping
database, which includes a labeled dataset of samples; S2, positioning of a multimodal acquisition terminal and acquisition of
multimodal data; S3,
signal analysis and diagnosis through a fusion
diagnostic model, including a two-
level fusion decision model. In the first-
level fusion part, the two-
level fusion decision model forms a multimodal fusion
feature vector; in the second-level fusion part, it analyzes and obtains diagnostic results and
abnormality diagnostic results for ECG signals, and then weightedly fuses them to output the final diagnostic result. This invention can accurately distinguish between
electrode displacement and actual
pathological abnormalities, reducing the
false positive rate, decreasing clinical misdiagnosis, and improving the reliability and efficiency of diagnostic results.