Search results for "multimodality analysis"

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A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series

2021

Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…

aikasarjatComputer science02 engineering and technologytransfer learningMachine learningcomputer.software_genreConvolutional neural networkuni (lepotila)polysomnography0202 electrical engineering electronic engineering information engineeringSleep researchFeature (machine learning)aivotutkimusBlock (data storage)multimodality analysissignaalinkäsittelybusiness.industryunitutkimusDeep learningSleep laboratorySIGNAL (programming language)deep learningsignaalianalyysi020206 networking & telecommunicationsautomatic sleep scoringkoneoppiminen020201 artificial intelligence & image processingArtificial intelligenceSleep (system call)businesscomputer2020 28th European Signal Processing Conference (EUSIPCO)
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