AIMC Topic: Signal Processing, Computer-Assisted

Clear Filters Showing 671 to 680 of 2081 articles

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Physical and engineering sciences in medicine
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose s...

Classification Method of ECG Signals Based on RANet.

Cardiovascular engineering and technology
BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias.

Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation.

IEEE transactions on bio-medical engineering
OBJECTIVE: The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position wi...

Physics-Informed Transfer Learning to Enhance Sleep Staging.

IEEE transactions on bio-medical engineering
OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hospital polysomnography due to its lower cost and lower disruption to the daily lives of patients, especially in the case of long-term monitoring. Machi...

Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces.

IEEE transactions on bio-medical engineering
OBJECTIVE: Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models,...

Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration.

Sensors (Basel, Switzerland)
Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern...

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.

Journal of neuroscience methods
BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals f...

Imagined speech classification exploiting EEG power spectrum features.

Medical & biological engineering & computing
Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagin...

Application of Statistical Analysis and Machine Learning to Identify Infants' Abnormal Suckling Behavior.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices.

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which featu...