Attention mechanism-based short-time single-lead electrocardiosignal atrial fibrillation automatic detection system

Abstract

The invention discloses an attention mechanism-based short-time single-lead electrocardiosignal atrial fibrillation automatic detection system, which is characterized by comprising a data sampling module, a preprocessing module, an atrial fibrillation automatic detection module and an optimization training module. According to the invention, the adaptive attention module is added, so that characteristics of a large number of electrocardiosignals can be accurately extracted, and redundant characteristics are removed; and meanwhile, by emphasizing and suppressing information, parameters in a network structure can be continuously updated and adjusted to pay attention to and retain some important electrocardiosignal characteristics. In addition, the bidirectional time domain sampling module is cascaded with the one-dimensional dense connection network, so that on one hand, more information of tiny fine-grained change related to the short-time single-lead atrial fibrillation signal can be extracted; on the other hand, the time domain features of the electrocardiosignals can be fully considered, the difference and complementarity between the atrial fibrillation signals are better concerned, and higher classification accuracy and specificity are obtained.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and electrocardiographic signal recognition, in particular to a short-term single-lead electrocardiographic signal automatic detection system for atrial fibrillation based on an adaptive attention mechanism. Background technique [0002] Atrial fibrillation is the most common and potentially harmful sustained arrhythmia clinically. It can easily cause malignant diseases, such as stroke, heart failure and thromboembolism. Due to its strong invisibility and the serious impact of various complications, atrial fibrillation poses a great threat to human life and health. The electrocardiogram, as the main detection method for atrial fibrillation, can identify and detect different types of arrhythmia diseases. Usually, during the process of recording ECG data, some patients may have just experienced an episode of atrial fibrillation, so that the ECG data of the atrial fibrillation segmen...

AI Technical Summary

Problems solved by technology

However, accurate, reliable, and portable detection of AF from short-duration single-lead ECG signals remains a great challenge due to limited beat information.

More importantly, only based on the conventional deep convolutional network model, there is no in-depth analysis of the ECG time-domain signal, nor does it consider the difference and complementarity of the ECG signal in the expression of different network features, resulting in atrial The accuracy and specificity of tremor signal detection is not high

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