![]() To avoid dense connections in DNN, CNNs introduces local connections and parameter sharing through convolution operations, which demonstrated numerous successes in computer vision application such as object recognition. The standard deep neural networks (DNN) consist of multiple layers of perceptrons, which all fully connected across consecutive layers. The most basic computation unit in neural networks is a perceptron which performs linear combinations of input features followed by a nonlinear transformation. 15–30ĭescription of deep neural network developmentĭeep learning algorithms such as multi-layer perceptrons, convolutional neural networks (CNN), and recurrent neural networks (RNN) have been successfully applied to many domains to solve challenging tasks. Prior work, for comparison, uses mainly small datasets, and mainly of healthy adults. Our system uses a unified deep network architecture (RCNN) to accomplish all 3 tasks. No prior study has simultaneously addressed all 3 key types of PSG information extracted by expert scorers: sleep stages, respiratory events, and limb movements. 14 In this paper, we outline the development of a Recurrent Convolutional Neural Network (RCNN) that matches the performance of sleep experts in annotating overnight PSGs. 12 Our system is trained end-to-end, directly from labeled signals.ĭeep neural networks, fueled by increases in computing power and availability of large labeled datasets, have recently matched the performance of medical experts in complex medical pattern recognition tasks such as visual diagnosis of dermatologic lesions 13 and diabetic retinopathy. Most prior approaches involve preprocessing and extraction of carefully engineered features before classification. This real-world data, recorded over 8 years in a clinical sleep laboratory, makes our PSG analysis system robust to physiologic variability between patients. We address this variability using a data-driven approach based on 79 456 hours of clinical data from 10 000 nights of PSG recording. 12 Models trained on such datasets are not likely to generalize well, because PSG signals vary widely due to differences in demographics, medication effects, sleep conditions, and medical conditions. Previous attempts to automate diagnosis of sleep disorders have generally relied on fewer than 100 PSGs from relatively homogeneous groups of healthy individuals. 10, 11 Recent advances in portable monitoring technology have increased access to sleep diagnostics, yet both at-home and the gold-standard in-lab polysomnography (PSG) still require manual scoring. ![]() 7, 8 Timely and accurate diagnosis of sleep disorders is critical to pursue appropriate treatment and improve health outcomes, 9 yet most sleep disorders remain undiagnosed. 1–7 The population health impact is enormous, including medical and psychiatric morbidity, motor vehicle accidents, decreased work productivity and quality of life, and increased mortality. Common sleep disorders such as sleep apnea, insomnia, and restless legs syndrome impact tens of millions of adults and are significant risk factors for cardiometabolic and neurodegenerative diseases, impaired performance, and decreased quality of life.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |