Sleep Apnea-Hypopnea Syndrome (SAHS) is a sleep-related breathing disorder which involves the reduction in breathing airway when patiens sleep. However, a large proportion of patients are usually undiagnosed and untreated which may lead to the risk of life. In this paper, we propose an automatic SAHS event detection method based on Long Short-Term Memory (LSTM) network via nasal airway pressure and temperature signals from clinical polysomnography (PSG) dataset. Focusing on time location of the events, we firstly segment the two channels of signals into a series of sequences by feature extraction. Secondly, a LSTM network is established and these sequences are subsequently fed into this LSTM network for SAHS event classification. The experimental results on both our clinical PSG dataset and public MIT-BIH PSG database show that our method is promising in terms of recall.