Deep Learning-Driven Intelligent Detection of Electrocardiogram Signals
This study addresses the challenges of electrocardiogram(ECG)signal classification by proposing methods based on three different models:Convolutional Neural Networks(CNN),CNN combined with Long Short-Term Memory networks(CNN+LSTM),and Deep Multi-Module Fusion Neural Network(DMMF-Net).These models,extensively compared and analyzed,aim to tackle the complexity and data imbalance issues in ECG classification.DMMF-Net introduces several innovative technical modules,including residual blocks,channel attention mechanisms,and multi-head self-attention mechanisms.Firstly,residual blocks ad-dress the gradient problem in deep network training,enabling the construction of deeper networks.Secondly,the channel attention mechanism dynamically adjusts the weights of feature maps to highlight crucial information.Thirdly,multi-head self-attention mech-anisms enhance the capture of long-range dependencies in sequential data.Additionally,we employ the focal loss function to address data imbalance issues,effectively balancing the weights of samples from different categories and improving model performance.By integrating these models and innovative modules,our research provides a more accurate and reliable tool for the early diagnosis and treatment of heart diseases,with the potential to enhance patients'quality of life and alleviate the burden on the healthcare system.
万方数据
维普
