We present a method for classifying target sleep arousal regions of polysomnographies. Time-and frequencydomain features of clinical and statistical origins were derived from the polysomnography signals and the features fed into a Bidirectional Recurrent Neural Network, using Long Short-Term Memory units (BRNN-LSTM). The predictions of five recurrent neural networks, trained using different features and training sets, were averaged for each sample, to yield a more robust classifier. The proposed method was developed and validated on the PhysioNet Challenge dataset which consisted of a training set of 994 subjects and a hidden test set of 989 subjects. Five-fold cross-validation on the training set resulted in an area under precision-recall curve (AUPRC) score of 0.452, an area under receiver operating characteristic curve (AUROC) score of 0.901 and intraclass correlation ICC(2,1) of 0.59. The classifier was further validated on the PhysioNet Challenge test set, resulting in an AUPRC score of 0.45.
Interactive learning has been suggested as a key method for addressing analytic multimedia tasks arising in several domains. Until recently, however, methods to maintain interactive performance at the scale of today's media collections have not been addressed. We propose an interactive learning approach that builds on and extends the state of the art in user relevance feedback systems and high-dimensional indexing for multimedia. We report on a detailed experimental study using the ImageNet and YFCC100M collections, containing 14 million and 100 million images respectively. The proposed approach outperforms the relevant state-of-the-art approaches in terms of interactive performance, while improving suggestion relevance in some cases. In particular, even on YFCC100M, our approach requires less than 0.3 s per interaction round to generate suggestions, using a single computing core and less than 7 GB of main memory.
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