深度学习驱动的心电图信号智能检测

Deep Learning-Driven Intelligent Detectionof Electrocardiogram Signals

针对心电图(Electrocardiogram,ECG)信号分类的复杂性和数据不平衡问题,提出了三种不同模型:卷积神经网络(Convolutional Neural Network,CNN)、卷积神经网络与长短时记忆网络(Long Short-Term Memory Network,LSTM)结合以及深层多模块融合神经网络(Deep Multi-Module Fusion Neural Network,DMMF-Net)。这些模型经过深入比较和分析,旨在解决ECG分类中的挑战。DMMF-Net引入了创新技术模块,包括残差块、通道注意力机制和多头自注意力机制。残差块解决了梯度消失的问题,允许构建更深层次的网络;通道注意力机制调整特征图的权重,以突出关键信息;多头自注意力机制有助于捕获序列数据中的长距离依赖关系。此外,采用Focal loss函数来处理数据不平衡,平衡了不同类别的样本权重,提高了模型性能。综合这些模型和创新模块,本研究为心脏疾病的早期诊断和治疗提供了更准确可靠的工具,以提高患者的生活质量并减轻医疗系统的负担。

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 address 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 mechanisms 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.