AIMC Topic: Signal Processing, Computer-Assisted

Clear Filters Showing 1781 to 1790 of 2081 articles

Research on emotion recognition using sparse EEG channels and cross-subject modeling based on CNN-KAN-[Formula: see text] model.

PloS one
Emotion recognition plays a significant role in artificial intelligence and human-computer interaction. Electroencephalography (EEG) signals, due to their ability to directly reflect brain activity, have become an essential tool in emotion recognitio...

Neural-WDRC: A Deep Learning Wide Dynamic Range Compression Method Combined With Controllable Noise Reduction for Hearing Aids.

Trends in hearing
Wide dynamic range compression (WDRC) and noise reduction both play important roles in hearing aids. WDRC provides level-dependent amplification so that the level of sound produced by the hearing aid falls between the hearing threshold and the highes...

The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks.

The Journal of the Acoustical Society of America
Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional gene...

Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods.

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

Current aging science
BACKGROUND: Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally le...

[A novel approach for assessing quality of electrocardiogram signal by integrating multi-scale temporal features].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed with the signal, potentially hindering doctors' ability to accurately assess and interpret patient data. Therefore, evaluating the quality of ECG sign...

[Gesture accuracy recognition based on grayscale image of surface electromyogram signal and multi-view convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers...

[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and...

A Feature Fusion Model Based on Temporal Convolutional Network for Automatic Sleep Staging Using Single-Channel EEG.

IEEE journal of biomedical and health informatics
Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we proposed a novel deep learning algorithm named feature fusion temporal convolutional network (FFTCN...

[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: We propose an autoencoder model based on a one-dimensional convolutional neural network (1DCNN) as the feature extraction network for efficient detection of epileptic EEG anomalies.