Aiming at the correlation problem between sleep stages and sleep apnea hypopnea, a sleep physiological time series classification method based on adaptive multi-task learning was proposed. Single-channel electroencephalogram and electrocardiogram were used for sleep staging and Sleep Apnea Hypopnea Syndrome (SAHS) detection. A two-stream time dependence learning module was utilized to extract shared features under joint supervision of the two tasks. The correlation between sleep stages and sleep apnea hypopnea was modeled by the adaptive inter-task correlation learning module with channel attention mechanism. The experimental results on two public datasets indicate that the proposed method can complete sleep staging and SAHS detection simultaneously. On UCD dataset, the accuracy, MF1(Macro F1-score), and Area Under the receiver characteristic Curve (AUC) for sleep staging of the proposed method were 1.21 percentage points, 1.22 percentage points, and 0.008 3 higher than those of TinySleepNet; its MF2 (Macro F2-score), AUC, and recall of SAHS detection were 11.08 percentage points, 0.053 7, and 15.75 percentage points higher than those of the 6-layer CNN model, which meant more disease segments could be detected. The proposed method could be applied to home sleep monitoring or mobile medical to achieve efficient and convenient sleep quality assessment, assisting doctors in preliminary diagnosis of SAHS.