Research on ECG signal feature point detection algorithm based on deep learning
LIANG Xiaohong
SONG Ningning
LIU Chengyou
TIAN Shuchang
ZHANG Huawei
QIN Hang
Abstract：In order to realize automatic,accurate and effective analysis of ECG data,we proposed a deep learning based ECG intel-ligent analysis model ECG SegNet to identify P waves,QRS complexes and T waves,and detect these waveforms' onsets and offsets.Firstly,the standard dilated convolution module was introduced into the encoder path to extract more ECG signal features.Then the bi-directional long term and short term memory was added to the encoding structure,to obtain numerous temporal features.In addition,the feature sets of each level in the encoder path were connected to the decoder part for multi-scale decoding to mitigate the information loss in the encoding process.Finally,the model was trained and tested on QT and LU databases respectively.On the QT database,the average F1 of P wave,QRS complex and T wave detection was 99.53%,99.82%,99.41%,respectively.On the LU database,the av-erage F1 of P wave,QRS complex and T wave detection was 94.74%,98.88%,97.53%,respectively.The results show that the model has good flexibility and reliability when applied to ECG signals detection,and it is a reliable method for analyzing ECG signals in real-time.
Keywords：ElectrocardiogramDeep learningEncoder-decoder structureConvolution neural networkBidirectional long short-term memory
Publication Date：2023-12-25
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 329-336 )