AIMC Topic: Signal Processing, Computer-Assisted
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A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.
Scientific reports
Mar 6, 2025
This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequenc...
On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network.
IEEE journal of biomedical and health informatics
Mar 6, 2025
The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neural networks, taking advantage of EEG's temporal resolution and the computational capabilities of embe...
Human Activity Recognition Using Deep Residual Convolutional Network Based on Wearable Sensors.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Human activity recognition (HAR) can play a vital role in biomedical and health informatics by enabling the monitoring of human daily activities and health behaviors. Accurate HAR can provide valuable insights into patients' physical activity levels,...
Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To addres...
Unsupervised Neural Decoding to Predict Dexterous Multi-Finger Flexion and Extension Forces.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Accurate control over individual fingers of robotic hands is essential for the progression of human-robot interactions. Accurate prediction of finger forces becomes imperative in this context. The state-of-the-art neural decoders can extract neural s...
AdaptEEG: A Deep Subdomain Adaptation Network With Class Confusion Loss for Cross-Subject Mental Workload Classification.
IEEE journal of biomedical and health informatics
Mar 6, 2025
EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation...
EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learn...
REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.
IEEE journal of biomedical and health informatics
Mar 6, 2025
High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2...
Near-lossless EEG signal compression using a convolutional autoencoder: Case study for 256-channel binocular rivalry dataset.
Computers in biology and medicine
Mar 5, 2025
Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, ...
An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.
Brain topography
Mar 4, 2025
EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks e...
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