AIMC Topic: Signal Processing, Computer-Assisted

Clear Filters Showing 1691 to 1700 of 2081 articles

PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.

Neural networks : the official journal of the International Neural Network Society
As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to decode human intention, promoting the development of human-computer interaction systems. Extensive ...

GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.

IEEE transactions on bio-medical engineering
Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These n...

Evaluation of a Low-Cost Amplifier With System Optimization in Thermoacoustic Tomography: Characterization and Imaging of Ex-Vivo and In-Vivo Samples.

IEEE transactions on bio-medical engineering
Microwave-induced thermoacoustic tomography (TAT) is a hybrid imaging technique that combines microwave excitation with ultrasound detection to create detailed images of biological tissue. Most TAT systems require a costly amplification system (or a ...

A Novel NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model.

IEEE transactions on bio-medical engineering
The examination of sleep patterns in newborns, particularly premature infants, is crucial for understanding neonatal development. This study presents an automated multi-sleep state classification approach for infants in neonatal intensive care units ...

mmWave Radar for Sit-to-Stand Analysis: A Comparative Study With Wearables and Kinect.

IEEE transactions on bio-medical engineering
This study investigates a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology, aiming to develop a non-contact, privacy-preserving, and all-day operational solution for healthcare applications. A ...

Machine learning in biosignal analysis from wearable devices.

Materials horizons
The advancement of wearable bioelectronics has significantly improved real-time biosignal monitoring, enabling continuous health tracking and providing personalized medical insights. However, the sheer volume and complexity of biosignal data collecte...

Bedside Ultrasound Vector Doppler Imaging System With GPU Processing and Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Recent innovations in vector flow imaging promise to bring the modality closer to clinical application and allow for more comprehensive, high frame-rate vascular assessments. One such innovation is plane-wave multi-angle vector Doppler, where pulsed ...

Empirical mode decomposition in clinical signal analysis: A systematic review.

Computers in biology and medicine
This systematic review examines the transformative applications of empirical mode decomposition (EMD) in healthcare, focusing on its ability to analyse diverse physiological signals. By a thorough exploration of key databases and stringent study sele...

A hybrid approach for machine learning based beat classification of ECG using different digital differentiators and DTCWT.

Computers in biology and medicine
This research paper presents a systematic approach to ECG beat classification using advanced machine learning techniques. The study classifies ECG beats into six distinct classes based on annotations from the MIT-BIH Arrhythmia Database. The methodol...

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

Computer methods in biomechanics and biomedical engineering
This study aims to introduce a novel, computationally lightweight feature extraction technique called Sliding Euclidean Pattern Quantization (SEPQ), which encodes local morphological patterns of ECG signals using Euclidean distance-based binary repre...