SCH: INT: Collaborative Research: DeepSense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine

点击翻译按钮获取中文摘要

Sleep represents one third of everyone’s life and affects the quality and the health of everyone’s life. Traditionally, long-term characteristics of sleep patterns (sleep phenotypes) are largely unknown due to the lack of convenient monitoring devices and automatic algorithms. Recently, massive health sensing data such as activity data, electroencephalogram, respiratory monitoring data and electrocardiography are being collected in clinics and at home, which brings unprecedented opportunities for understanding sleep phenotypes outside clinics. However, there are tremendous challenges to translate these noisy and unreliable multimodal sensing data into accurate phenotypes such as sleep stages and apnea events. Beyond sleep, many neurological conditions such as Alzheimer’s and Parkinson’s all expect objective tracking of long-term disease progression, which is currently impossible. This project will provide the computational capability to conduct phenotype tracking at home with the focus on sleep phenotypes. Machine learning methods and software will be developed to conduct accurate phenotyping of sleep with minimal effort on sensor instrumentation, data collection and analysis. This project aims at developing DeepSense, a deep learning toolbox to model massive data streams including in-clinic monitoring data such as polysomnography and novel radio frequency signals from a wireless sensing device. The research team will develop accurate deep learning methods to automate sleep monitoring using polysomnography data. They will invent adversarial deep learning methods for modeling radio frequency signals and leverage large historical polysomnography data to help improve models for radio frequency signals. They will develop interpretable models that leverage and expand medical knowledge on sleep phenotypes. Finally, all the proposed models will be validated through a prospective study with a goal of automating manual sleep studies and assessing the feasibility of sleep studies via radio frequency signal data. The research team plans to release the open-source software and large datasets from this project that can benefit computer science, engineering and medical community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.