计算机与现代化Issue(8)：17-25,34,10.DOI:10.3969/j.issn.1006-2475.2026.08.003
融合频域注意力与时空特征的睡眠呼吸事件分类识别模型
Fusion Model of Frequency-Domain Attention and Spatiotemporal Features for Sleep Respiratory Event Classification
摘要
Abstract
Accurate classification of sleep respiratory events is crucial for diagnosing Obstructive Sleep Apnea(OSA)and Central Sleep Apnea(CSA).However,existing methods often suffer from high-frequency noise interference and insufficient coverage of pathologically sensitive frequency bands during time-frequency feature extraction and fusion.To address this issue,we propose CBFA,a classification algorithm that integrates frequency-domain attention with spatio-temporal features.The model adopts a hi-erarchical deep neural network architecture,where an improved CNN14 extracts discriminative frequency-domain features and BiLSTM captures long-range temporal dynamics.A frequency-domain attention module(FreqAttention)is further introduced to enhance representations within the 0~6000 Hz pathological band.Experiments on the PSG-Audio multimodal dataset demon-strate that CBFA algorithm effectively suppresses high-frequency noise and improves the representation of low-frequency fea-tures.Ablation studies confirm the contribution of BiLSTM to temporal modeling,increasing CSA recall from 0.71 to 0.83(+12 percentage points),and the benefit of FreqAttention for OSA detection,raising recall from 0.88 to 0.96(+8 points).Compared with existing approaches such as VGG_LSTM and CNRR,CBFA achieves superior performance,with an accuracy of 92%,a macro-average F1 score of 0.82,and a weighted F1 score of 0.93.The results indicate that CBFA algorithm can effectively reduce the impact of high-frequency noise,enhance the representation of pathologically sensitive frequency bands,and achieve superior performance in classifying OSA and CSA.This provides an efficient and reliable solution for automated audio-based detection of sleep respirat-ory events.关键词
睡眠呼吸障碍/音频信号处理/频域注意力机制/时空特征融合/双向长短期记忆网络Key words
sleep apnea/audio signal processing/frequency domain attention mechanism/spatio-temporal feature fusion/bidi-rectional long short-term memory network分类
信息技术与安全科学引用本文复制引用
王继红,林冰生..融合频域注意力与时空特征的睡眠呼吸事件分类识别模型[J].计算机与现代化,2026,(8):17-25,34,10.基金项目
2024年广东省教育科学规划课题(高等教育专项)(2024GXJK455) （高等教育专项）
2024年广东省高等教育教学研究和改革项目"基于深度学习的学生画像与学业预警模型构建研究"(粤教高函[2024]30号) （粤教高函[2024]30号）
