AIMC Topic: Signal Processing, Computer-Assisted

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A deep learning approach to estimate pulse rate by remote photoplethysmography.

Physiological measurement Jul 25, 2022
This study proposes a U-net shaped Deep Neural Network (DNN) model to extract remote photoplethysmography (rPPG) signals from skin color signals to estimate Pulse Rate (PR).Three input window sizes are used in the DNN: 256 samples (5.12 s), 512 sampl...
Neural Networks, Computer Algorithms Signal Processing, Computer-Assisted Heart Rate Deep Learning Photoplethysmography
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Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network.

Computational intelligence and neuroscience Jul 21, 2022
The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the ge...
Signal Processing, Computer-Assisted Neural Networks, Computer Molybdenum Electroencephalography Humans
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A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Computers in biology and medicine Jul 14, 2022
Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signa...
Machine Learning Electroencephalography Humans Electrooculography Sleep Stages Signal Processing, Computer-Assisted Polysomnography
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Multimodal Sensors with Decoupled Sensing Mechanisms.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jul 14, 2022
Highly sensitive and multimodal sensors have recently emerged for a wide range of applications, including epidermal electronics, robotics, health-monitoring devices and human-machine interfaces. However, cross-sensitivity prevents accurate measuremen...
Electronics Robotics Signal Processing, Computer-Assisted Humans Wearable Electronic Devices
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A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.

BioMed research international Jul 4, 2022
Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are a...
Deep Learning Electrocardiography Algorithms Humans Databases, Factual Diagnostic Imaging Arrhythmias, Cardiac Neural Networks, Computer Signal Processing, Computer-Assisted Heart Rate
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Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

Methods (San Diego, Calif.) Jul 1, 2022
The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. Recent studies have shown that radar-based non-contact vital sign monitoring system can effectively d...
Sleep Apnea Syndromes Polysomnography Humans Machine Learning Signal Processing, Computer-Assisted
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DC Motor Control Technology Based on Multisensor Information Fusion.

Computational intelligence and neuroscience Jul 1, 2022
To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one...
Signal Processing, Computer-Assisted Uncertainty Algorithms Technology Neural Networks, Computer
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Drowsiness Detection Using Ocular Indices from EEG Signal.

Sensors (Basel, Switzerland) Jun 24, 2022
Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. Howeve...
Signal Processing, Computer-Assisted Support Vector Machine Electroencephalography Algorithms Humans Machine Learning
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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Computational intelligence and neuroscience Jun 17, 2022
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches ha...
Humans Seizures Algorithms Signal Processing, Computer-Assisted Electroencephalography Deep Learning Epilepsy Support Vector Machine
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Approximation in shift-invariant spaces with deep ReLU neural networks.

Neural networks : the official journal of the International Neural Network Society Jun 16, 2022
We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processing, communications and so on. Approximation error bounds are estimated...
Signal Processing, Computer-Assisted Neural Networks, Computer Communication Image Processing, Computer-Assisted
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