实用心电与临床诊疗2025，Vol.34Issue(3)：319-326,8.DOI:10.13308/j.issn.2097-5716.2025.03.002
基于迁移学习与心电信号的睡眠呼吸暂停综合征分类模型构建
Development of a sleep apnea syndrome monitoring model using transfer learning with ECG signals
摘要
Abstract
Objective To develop a transfer learning-based classification model for sleep apnea syndrome using electrocardiogram(ECG)data,increasing its classification accuracy and clinical applicability.Methods Based on the Apnea-ECG and MIT-BIH polysomnographic databases,with respiratory signals as input,we applied a Butterworth low-pass filter for denoising,and constructed an original data set.To address the problem of insufficient respiratory signal data,a model training method based on a transfer learning approach was proposed:first,ECG signals with a large sample size were used for model pre-training,and then they were fine-tuning for respiratory signals,finally fulfilling binary classification or multi-class classification tasks.A cascade model combining residual network and bidirectional long short-term memory network was proposed,which performed better in capturing the timing features of signals and improving classification performance.Additionally,the performance of this model was made comparative analysis with those of various classic convolutional neural networks.Results Through comparative experiments,it was found that employing transfer learning approach could accelerate model convergence and improve the model's overall performance.Validated on the test set,the proposed cascade model demonstrated a favorable performance in both binary classification and multi-class classification tasks,achieving an accuracy of 95.43％ on the binary classification task and 91.26％on the multi-class classification task.Conclusion This study offers novel insights into the design of disease classification models under small-sample conditions,and validates the effectiveness of transfer learning in sleep apnea syndrome classification,thereby demonstrating its potential clinical utility.关键词
人工智能/长短时记忆网络/睡眠呼吸暂停综合征/迁移学习Key words
artificial Intelligence/long short-term memory network/sleep apnea syndrome/transfer learning分类
医药卫生引用本文复制引用
樊明辉,谢锦成,王量弘,张希铃,王新康..基于迁移学习与心电信号的睡眠呼吸暂停综合征分类模型构建[J].实用心电与临床诊疗,2025,34(3):319-326,8.基金项目
国家自然科学基金资助项目(61971140) （61971140）
福建省财政厅项目(闽财指[2023]891号) （闽财指[2023]891号）
