AIMC Topic: Signal Processing, Computer-Assisted
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Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.
International journal of neural systems
Mar 29, 2025
Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accurate and real-time automatic seizure detection system is crucial for assisting clinicians in making ...
Flexible Patched Brain Transformer model for EEG decoding.
Scientific reports
Mar 29, 2025
Decoding the human brain using non-invasive methods is a significant challenge. This study aims to enhance electroencephalography (EEG) decoding by developing of machine learning methods. Specifically, we propose the novel, attention-based Patched Br...
A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches.
Sensors (Basel, Switzerland)
Mar 27, 2025
Sarcopenia, affecting between 1-29% of the older population, is characterized by an age-related loss of skeletal muscle mass and function. Reduced muscle strength, either in terms of quantity or quality, and poor physical performance are among the cr...
Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.
Scientific reports
Mar 27, 2025
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Poss...
Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.
Scientific reports
Mar 25, 2025
A heart murmur is an atypical sound produced by blood flow through the heart. It can indicate a serious heart condition, so detecting heart murmurs is critical for identifying and managing cardiovascular diseases. However, current methods for identif...
Synthetic ECG signal generation using generative neural networks.
PloS one
Mar 25, 2025
Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases. Additionally, the use of real patients' ECGs is highly regulated due to privacy issues. Therefore, there is always a need for more ECG data, especial...
Multiple Instance Learning-Based Prediction of Blood-Brain Barrier Opening Outcomes Induced by Focused Ultrasound.
IEEE transactions on bio-medical engineering
Mar 21, 2025
OBJECTIVE: Targeted blood-brain barrier (BBB) opening using focused ultrasound (FUS) and micro/nanobubbles is a promising method for brain drug delivery. This study aims to explore the feasibility of multiple instance learning (MIL) in accurate and f...
Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation Using Recursive Spiking Neural Networks.
IEEE transactions on bio-medical engineering
Mar 21, 2025
OBJECTIVE: To develop a novel method for improved screening of sleep apnea in home environments, focusing on reliable estimation of the Apnea-Hypopnea Index (AHI) without the need for highly precise event localization.
PULSE: A DL-Assisted Physics-Based Approach to the Inverse Problem of Electrocardiography.
IEEE transactions on bio-medical engineering
Mar 21, 2025
This study introduces an innovative approach combining deep-learning techniques with classical physics-based electrocardiographic imaging (ECGI) methods. Our objective is to enhance the accuracy and robustness of ECGI reconstructions. We reshape the ...
Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.
IEEE transactions on bio-medical engineering
Mar 21, 2025
For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas (AP) systems. Deep learning frameworks like Long Short-Term-Memory (LSTM) have been widely used to ...
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