AIMC Topic: Signal Processing, Computer-Assisted

Clear Filters Showing 1751 to 1760 of 2081 articles

An Ultra-Low Power Wearable BMI System With Continual Learning Capabilities.

IEEE transactions on biomedical circuits and systems
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However,...

NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network With a Diamond Topology for Real-Time Data Processing.

IEEE transactions on biomedical circuits and systems
The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike ...

Ultra-Low Power Analog Folded Neural Network for Cardiovascular Health Monitoring.

IEEE journal of biomedical and health informatics
Wearable sensors are increasingly used for continuous health monitoring, but their small size limits battery capacity, affecting user experience and monitoring capabilities. To overcome this, we introduce an ultra-low power analog Folded Neural Netwo...

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising.

IEEE journal of biomedical and health informatics
Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts is an essential step in practice. As of now, deep learning-based E...

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Biomedical physics & engineering express
Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and physiological variability. Manual artifact detection is labor-intensive and subjective, underscoring the...

An ensemble deep learning framework for emotion recognition through wearable devices multi-modal physiological signals.

Scientific reports
The widespread availability of miniaturized wearable fitness trackers has enabled the monitoring of various essential health parameters. Utilizing wearable technology for precise emotion recognition during human and computer interactions can facilita...

Optimized deep residual networks for early detection of myocardial infarction from ECG signals.

BMC cardiovascular disorders
Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to ...

Investigating the correlation between smoking and blood pressure via photoplethysmography.

Biomedical engineering online
Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological sig...

Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network.

Scientific reports
Epilepsy is a neurological disorder characterized by recurrent seizures caused by excessive electrical discharges in brain cells, posing significant diagnostic and therapeutic challenges. Dynamic brain network analysis via electroencephalography (EEG...

Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic-Ischemic Encephalopathy Injury.

Sensors (Basel, Switzerland)
Hypoxic-Ischemic Encephalopathy (HIE) occurs in patients who experience a decreased flow of blood and oxygen to the brain, with the optimal window for effective treatment being within the first six hours of life. This puts a significant demand on med...