The invention discloses an
atrial fibrillation anomaly detection method and
system based on multi-sensing time-frequency pseudo anomalies, and the method comprises the steps: obtaining an atrial electrophysiological
signal sequence through an HDS high-density sensing
atrial fibrillation analysis platform, carrying out the time-
frequency conversion of the
signal sequence, constructing a multi-
modal time-frequency
tensor model, and extracting a time-frequency feature subset related to the
atrial fibrillation anomaly; dynamically distributing feature weights by utilizing an
extrusion excitation time-frequency pseudo-selection model so as to optimize features; constructing a double-domain
hypersphere feature space based on the optimized features, calculating a pseudo-anomaly distance value through a double-domain
hypersphere pseudo-anomaly distance
calculation algorithm, and dividing a suspected atrial
fibrillation anomaly feature sample set in combination with a preset threshold; and carrying out
feature fusion on the suspected abnormal sample set, extracting common abnormal features and generating a detection result. The
system corresponds to six units, the comprehensiveness of
feature extraction and the accuracy of anomaly recognition are greatly improved, the clinical requirements for atrial
fibrillation detection are met, and reliable
technical support is provided for early diagnosis of atrial
fibrillation.