Middleton Burris (ballrouter49)

4% test error under 600M FLOPs with 8 GPUs in 18 hours. As for semantic segmentation task, DSO-NAS also achieve competitive result compared with manually designed architectures on PASCAL VOC dataset. Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. selleck kinase inhibitor The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small. As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small. A miniaturized accelerometer can be incorporated in temporary pacemaker leads which are routinely attached to the epicardium during cardiac surgery and provide continuous monitoring of cardiac motion during and following surgery. We tested if such a sensor could be used to assess volume status, which is essential in hemodynamically unstable patients. An accelerometer was attached to the epicardium of 9 pigs and recordings performed during baseline, fluid loading, and phlebotomy in a closed chest condition. Alterations in left ventricular (LV) preload alter myocardial tension which affects the frequency of myocardial acceleration associated with the first heart sound ( f ). The accuracy of f as an estimate of preload was evaluated using sonomicrometry measured end-diastolic volume ( EDV ). Standard clinical estimates of global end-diastolic volume using pulse index continuous cardiac output (PiCCO) measurements ( GEDV ) and pulmonary artery occlusion pressure (PAOP) were obtained for comparison. The diagnostic accuracy of identifying fluid responsiveness was analyzed for f , stroke volume variation ( SVV ), pulse pressure variation ( PPV ), GEDV , and PAOP. Changes in f f correlated well to changes in EDV (r^2=0.81, 95%CI [0.68, 0.89]), as did GEDV (r^2=0.59, 95%CI [0.36, 0.76]) and PAOP (r^2=0.36, 95%CI [0.01, 0.73]). The diagnostic accuracy [95%CI] in identifying fluid responsiveness was 0.79 [0.66, 0.94] for f , 0.72 [0.57, 0.86] for [Formula see text], and 0.63 (0.44, 0.82) for PAOP. An epicardially placed accelerometer can assess changes in preload in real-time. This novel method can facilitate continuous monitoring of the volemic status in open-heart surgery patients and help guiding fluid resuscitation. This novel method can facilitate continuous monitoring of