The invention relates to an electrocardiogram
signal classifying method based on one-dimensional
convolutional neural network. The method comprises the following steps: firstly, conducting denoising
processing on an electrocardiogram
signal by virtue of a
wavelet fusion method; then, detecting an R wave peak point by a QRS
wave group recognition algorithm based on biorthogonal
spline wavelet, and completing division and
dimensionality reduction of the electrocardiogram
signal with the R point as a datum, so that a plurality of R wave candidate bands are obtained; then, establishing and optimizing an electrocardiogram signal oriented one-dimensional
convolutional neural network model; and finally, with the processed R wave candidate bands as input data of the model, automatically completing characteristic extraction and classification of the electrocardiogram signal. The method provided by the invention, with the adoption of the
wavelet fusion method, can simultaneously remove high-
frequency noise and low-
frequency noise, so that extracted signal characteristics are more conducive to recognition; by establishing the electrocardiogram signal oriented one-dimensional
convolutional neural network model, the problem that electrocardiogram
signal characteristic points must be precisely located is solved, and moreover, the problem that a conventional method, which selects an
algorithm to extract characteristics firstly, and then selects an
algorithm to complete classification, is complex in computation is solved.