Search Results - "Convolutional Denoising Autoencoder"
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Noise Reduction in Photoplethysmography Signals using a Convolutional Denoising Autoencoder with Unconventional Training Scheme
ISSN: 0018-9294, 1558-2531, 1558-2531Published: United States IEEE 01.02.2024Published in IEEE transactions on biomedical engineering (01.02.2024)“…Objective : We propose an efficient approach based on a convolutional denoising autoencoder (CDA…”
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Journal Article -
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Automatic Eyeblink Artifact Removal from Single Channel EEG Signals Using One-Dimensional Convolutional Denoising Autoencoder
ISSN: 2768-0576Published: IEEE 02.02.2024Published in International Conference on Computer, Electrical & Communication Engineering (Online) (02.02.2024)“… In this work, we proposed a one-dimensional Convolutional Denoising Autoencoder (CDAE) architecture to efficiently remove the eyeblink artifacts from the single channel EEG signals…”
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Conference Proceeding -
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Atrial fibrillation detection on reconstructed photoplethysmography signals collected from a smartwatch using a denoising autoencoder
ISSN: 0957-4174, 1873-6793Published: Elsevier Ltd 01.03.2024Published in Expert systems with applications (01.03.2024)“…; however, the subjects were mostly in clinics or controlled settings with data collection lasting several minutes to at most several hours with minimal MNA…”
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Journal Article -
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Classification of massive noisy image using auto-encoders and convolutional neural network
Published: IEEE 01.05.2017Published in 2017 8th International Conference on Information Technology (ICIT) (01.05.2017)“… Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the image required to classify is subject to noise and other disfigurement…”
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Conference Proceeding -
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DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 25.01.2020Published in arXiv.org (25.01.2020)“…Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate…”
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