Electrocardiograph detection method based on multi-scale deep learning neural network
A neural network and deep learning technology, applied in the field of ECG detection, can solve the problems of short QRS wave time limit, large interference of ECG detection, and ineffective judgment of arrhythmia waveform, so as to improve the processing speed and accuracy rate.
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[0049] In order to better explain the present invention and facilitate understanding, the following describes the present invention in detail through specific embodiments in conjunction with the accompanying drawings.
[0050] All technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more related listed items.
[0051] In the embodiment of the present invention, a multi-scale deep convolutional artificial neural network algorithm is used to construct an arrhythmia detection model, and a series of ECG data to be analyzed are mapped to a series of determined arrhythmia data to realize intelligent detection of current ECG data.
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