使用异质集成学习和心电信号异构特征融合的睡眠呼吸暂停分类方法  

Sleep apnea classification method utilizing heterogeneous ensemble learning and electrocardiogram heterogeneous feature fusion

作　　者：韩亮[1,2] 罗统军 蒲秀娟 刘媛[1] 梁国祥 Han Liang;Luo Tongjun;Pu Xiujuan;Liu Yuan;Liang Guoxiang(School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China;Chongqing Key Laboratory of Bio-perception&Intelligent Information Processing,Chongqing 400044,China)

机构地区：[1]重庆大学微电子与通信工程学院,重庆400044 [2]生物感知与智能信息处理重庆市重点实验室,重庆400044

出　　处：《仪器仪表学报》2024年第6期320-327,共8页Chinese Journal of Scientific Instrument

摘　　要：睡眠呼吸暂停(SA)会影响睡眠质量,增加心脑血管疾病风险,其准确分类有助于在SA早期阶段及时开展针对性治疗。本文提出一种使用异质集成学习和异构特征融合的SA分类新方法。首先从原始心电信号中提取小波时频谱,使用SE-ResNet作为初级分类器;然后提取RR间期序列和R峰值序列,使用1D CNN-LSTM作为初级分类器;再提取心率变异性特征,使用SVM作为初级分类器。最后采用堆叠法作为异质集成学习的融合策略,再使用另一个SVM作为次级分类器实现SA分类。在Apnea-ECG数据集上进行实验,所提出的SA分类方法的准确率为89.12%。实验结果表明,所提方法有效利用了各初级分类器的多样性和异构特征的互补性,其性能优于传统的SA分类方法。Sleep apnea(SA)affects the quality of sleep and increases the risk of cerebrovascular and cardiovascular diseases.It is advantageous to implement the accurate classification for the is advantageous to timely treatment at the early stage of SA.In this paper,one novel SA classification method utilizing heterogeneous ensemble learning and heterogeneous feature fusion is proposed.Firstly,the SE-ResNet is used as primary classifier of the extracted wavelet time-frequency spectrum from raw electrocardiogram(ECG).Then the 1D CNN-LSTM is used as primary classifier of the extracted R-peak to R-peak interval(RRI)sequence and R-peak amplitude(RAMP)sequence.And the SVM is used as primary classifier of extracted heart rate variability features.Finally,the stacking method is adopted as fusion strategy for heterogeneous ensemble learning,and then another SVM is used as the secondary classifier to implement SA classification.The proposed SA classification method is evaluated on Apnea-ECG dataset,whose accuracy The accuracy of the proposed SA classification method is 89.12%.Experimental results show that the proposed method utilizes the diversity of primary classifiers and complementarity of heterogeneous features efficiently,which outperforms the and is better than other conventional SA classification method.

关 键 词：睡眠呼吸暂停 集成学习 异构特征融合 心电信号 深度学习 

分 类 号：TH701[机械工程—仪器科学与技术] TP391[机械工程—精密仪器及机械]