A Hybrid Transformer Model for Obstructive Sleep Apnea Detection Based on Self-Attention Mechanism Using Single-Lead ECG

Obstructive sleep apnea (OSA) is a widespread sleep disorder that seriously affects human health. Detection of OSA with low cost and high accuracy is of great clinical significance. This study proposes a hybrid transformer model based on self-attention mechanism for OSA detection using single-lead electrocardiogram. To reduce the introduction of expert knowledge, a new method for constructing raw inputs is proposed. The data inputs include the raw electrocardiogram (ECG) signal sequence, R-peak amplitude (RA) sequence, interbeat (RR) interval (RRI) sequence, and RR interval first-order difference (RRID) sequence. Then, a multiperspective channel-attention (MPCA) block is proposed to focus on the contribution of four input signals automatically and extract the fused multiperspective features. These features together with their position encodings were fed into transformer blocks to encode the most important information with self-attention mechanism. Finally, the prediction results were output through the linear layer. The proposed method was verified on the apnea-ECG database. The per-segment classification accuracy reached 0.91 and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.96. The per-recording classification accuracy reached 100% and the mean absolute error (MAE) was 2.71. Our method achieved better classification performance than other state-of-the-art algorithms. The proposed hybrid transformer model is able to detect OSA accurately using single-lead ECG in a manner similar to the detection process by human experts. Our method provides a convenient and precise solution for clinical OSA detection.