AIMC Topic: Signal Processing, Computer-Assisted

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Graph convolution network-based eeg signal analysis: a review.

Medical & biological engineering & computing Jan 30, 2025
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The appl...
Brain-Computer Interfaces Brain Electroencephalography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.

International journal of medical informatics Jan 30, 2025
BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and breathing. The electrocardiogram (ECG) stands as an essential tool extensively utilized by clinicians, ...
Humans Algorithms Databases, Factual Female Male Heart Arrest Deep Learning Electrocardiography Signal Processing, Computer-Assisted
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Gesture recognition from surface electromyography signals based on the SE-DenseNet network.

Biomedizinische Technik. Biomedical engineering Jan 29, 2025
OBJECTIVES: In recent years, significant progress has been made in the research of gesture recognition using surface electromyography (sEMG) signals based on machine learning and deep learning techniques. The main motivation for sEMG gesture recognit...
Signal Processing, Computer-Assisted Humans Electromyography Machine Learning Algorithms Pattern Recognition, Automated Convolutional Neural Networks Gestures
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tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

Physiological measurement Jan 29, 2025
In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes...
Electrocardiography Signal Processing, Computer-Assisted Atrial Fibrillation Neural Networks, Computer Humans Software Computers
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Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Physical and engineering sciences in medicine Jan 27, 2025
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment ...
Entropy Signal Processing, Computer-Assisted Sex Characteristics Sex Factors Algorithms Parkinson Disease Humans Brain Electroencephalography Female Machine Learning Aged Male Middle Aged
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Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage Jan 27, 2025
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...
Electroencephalography Signal Processing, Computer-Assisted Acoustic Stimulation Male Deep Learning Adolescent Evoked Potentials Loudness Perception Humans Brain Female Young Adult Adult
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Convolutional neural network-based method for the real-time detection of reflex syncope during head-up tilt test.

Computer methods and programs in biomedicine Jan 26, 2025
BACKGROUND AND OBJECTIVES: Reflex syncope (RS) is the most common type of syncope caused by dysregulation of the autonomic nervous system. Diagnosing RS typically involves the head-up tilt test (HUTT), which tracks physiological signals such as blood...
Tilt-Table Test Blood Pressure Signal Processing, Computer-Assisted Syncope Male Middle Aged Deep Learning Convolutional Neural Networks ROC Curve Algorithms Electrocardiography Humans Female Neural Networks, Computer Adult
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Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.

Computers in biology and medicine Jan 25, 2025
Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of...
Neural Networks, Computer Cardiovascular Diseases Deep Learning Humans Electrocardiography Signal Processing, Computer-Assisted
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Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition.

Sensors (Basel, Switzerland) Jan 25, 2025
Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convoluti...
Humans Human Activities Algorithms Neural Networks, Computer Fourier Analysis Deep Learning Radar Signal Processing, Computer-Assisted
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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion.

Sensors (Basel, Switzerland) Jan 23, 2025
Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management ...
Arrhythmias, Cardiac Algorithms Electrocardiography Signal Processing, Computer-Assisted Humans Neural Networks, Computer
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