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@conference{b24e96dd698643869a85f6769063fc71, author = "Jose Martin Perez-Macias and Sharath Adavanne and Jari Viik and Alpo V{\"a}rri and Sari-Leena Himanen and Mirja Tenhunen", abstract = "Snoring (SN) is an essential feature of sleep breathing disorders, such as obstructive sleep apnea (OSA). In this study, we evaluate epoch-based snoring detection methods using an unobtrusive electromechanical film transducer (Emfit) mattress sensor using polysomnography recordings as a reference. Two different approaches were investigated: a support vector machine (SVM) classifier fed with a subset of spectral features and convolutional neural network (CNN) fed with spectrograms. Representative 10-min normal breathing (NB) and SN periods were selected for analysis in 30 subjects and divided into thirty-second epochs. In the evaluation, average results over 10 fold Monte Carlo cross-validation with 80% training and 20% test split were reported. Highest performance was achieved using CNN, with 92% sensitivity, 96% specificity, 94% accuracy, and 0.983 area under the receiver operating characteristics curve (AROC). Results showed a 6% average increase of performance of the CNN over SVM and greater robustness, and similar performance to ambient microphones.", booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)", doi = "10.1109/EMBC.2017.8037459", month = "9", pages = "2883--2886", publisher = "IEEE", title = "{A}ssessment of support vector machines and convolutional neural networks to detect snoring using {E}mfit mattress", year = "2017", }