沉默是致命的：利用 Whisper V3 和 CNN 构建实时睡眠呼吸暂停预警系统

Sleep is crucial for bodily rejuvenation, yet millions suffering from Obstructive Sleep Apnea (OSA) face a nightly struggle for breath. Traditional polysomnography is both expensive and intrusive. This project explores leveraging smartphone microphones for real-time breathing pattern monitoring, offering a low-cost, non-invasive alternative for OSA detection. The system delves into real-time audio processing, Whisper V3 feature extraction, and CNN spectrogram analysis to construct a low-power, edge-compatible OSA warning system. By combining Whisper's state-of-the-art transformer architecture with CNNs' spatial pattern recognition capabilities, the system aims to identify dangerous breathing pauses before they escalate into emergencies. This innovative approach presents new possibilities for early detection and intervention in OSA, potentially improving patients' quality of life and lowering the barriers to traditional diagnostic methods. The technology holds significant promise for smart healthcare, revolutionizing personal health monitoring by making advanced diagnostic tools more accessible and integrated into daily life. The focus on edge compatibility ensures the system can operate efficiently on consumer devices, broadening its potential impact on public health.