TFFormer: A Time–Frequency Information Fusion-Based CNN-Transformer Model for OSA Detection With Single-Lead ECG

Accurate detection of Obstructive Sleep Apnea (OSA) with a single-lead electrocardiogram (ECG) signal is highly desirable for timely treating of OSA patients. However, due to the variance of apneas in appearance and size in ECG signals, it is still a very challenging task to obtain an accurate OSA apnea detection. To address this problem, this paper presents a time frequency information fusion based CNN-Transformer model (TFFormer) for OSA detection with Single-lead ECG. In which, a module consisting of a deep residual shrinkage module, a multi-scale convolutional attention module (MSCA), and a multi-layer convolution module is developed for time-frequency feature extraction. The purpose of this operation is to extract rich features from a short length of ECG signal sequences with a low computation cost. For time-frequency information fusion, to reduce its computation cost, a gated self-attention based adaptive pruning time-frequency information fusion module is developed to prune the redundant tokens. With the attention based adaptive pruning time-frequency information fusion module(APTFFA), the TFFormer is constructed for data parallel processing and long-distance modeling. Compared with the best model in the comparative method, the accuracy of the proposed method was improved by 0.18% in the segmented case, and the mean absolute error was reduced by 0.25 per-recorded case, which demonstrates that the TFFormer model has better OSA detection performance and could provide a convenient and accurate solution for clinical OSA detection.