Motion Process Electrocardiogram Detection Algorithm under Self Supervised Deep Learning

Electrocardiogram is a non-invasive detection technology that detects, predicts, and monitors cardiovascular diseases by reflecting the relationship between the electrical activity of the heart system and time. It does not cause any harm to patients and has a simple detection method and reliable diagnostic results. It plays an important role in the analysis and diagnosis of cardiovascular diseases. The electrocardiogram can automatically record the biological signals generated by myocardial contraction during the activity of the heart system, which is also known as the electrocardiogram signal. This article explores the motion process electrocardiogram detection algorithm based on convolutional neural networks algorithm based on the above issues. It has been experimentally proven that the detection rate of electrocardiogram detection during motion based on CNN algorithm is higher than 87%, with an average detection rate of 93.46%. The false alarm rate of electrocardiogram detection is less than 1%, with an average false alarm rate of 0.54%.