Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data
This work addresses the problem of automated sleep apnea diagnosis for undiagnosed patients by enhancing model reliability through multi-sensor fusion, though it is incremental as it builds on existing deep learning approaches.
The paper tackled sleep apnea detection by proposing a novel late sensor fusion method using backward shortcut connections to improve model learning from multi-modal data, achieving significant and consistent performance improvements over single-sensor and other fusion methods.
Sleep apnea is a common respiratory disorder characterized by breathing pauses during the night. Consequences of untreated sleep apnea can be severe. Still, many people remain undiagnosed due to shortages of hospital beds and trained sleep technicians. To assist in the diagnosis process, automated detection methods are being developed. Recent works have demonstrated that deep learning models can extract useful information from raw respiratory data and that such models can be used as a robust sleep apnea detector. However, trained sleep technicians take into account multiple sensor signals when annotating sleep recordings instead of relying on a single respiratory estimate. To improve the predictive performance and reliability of the models, early and late sensor fusion methods are explored in this work. In addition, a novel late sensor fusion method is proposed which uses backward shortcut connections to improve the learning of the first stages of the models. The performance of these fusion methods is analyzed using CNN as well as LSTM deep learning base-models. The results demonstrate a significant and consistent improvement in predictive performance over the single sensor methods and over the other explored sensor fusion methods, by using the proposed sensor fusion method with backward shortcut connections.