AIMC Topic: Signal Processing, Computer-Assisted

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Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Sensors (Basel, Switzerland) Feb 25, 2025
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw ...
Brain-Computer Interfaces Movement Imagination Algorithms Humans Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted
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Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Brain topography Feb 24, 2025
Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a number of diseases Due to inherent data uncertainties and Electroencephalogram (EEG) pattern overlap,...
Electroencephalography Humans Signal Processing, Computer-Assisted Neural Networks, Computer Nervous System Diseases Algorithms Brain Fuzzy Logic
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Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

Scientific reports Feb 21, 2025
While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the developmen...
Seizures Signal Processing, Computer-Assisted Aged Electroconvulsive Therapy Adult Middle Aged Male Humans Electroencephalography Machine Learning Female
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EffNet: an efficient one-dimensional convolutional neural networks for efficient classification of long-term ECG fragments.

Biomedical physics & engineering express Feb 21, 2025
Early Diagnosis of Cardiovascular disease (CVD) is essential to prevent a person from death in case of a cardiac arrhythmia. Automated ECG classification is required because manual classification by cardiologists is laborious, time-consuming, and pro...
Humans Neural Networks, Computer Algorithms Arrhythmias, Cardiac Convolutional Neural Networks Databases, Factual Electrocardiography Cardiovascular Diseases Signal Processing, Computer-Assisted
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Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

IEEE transactions on bio-medical engineering Feb 20, 2025
OBJECTIVE: Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR).
Ventricular Fibrillation Arrhythmias, Cardiac Humans Tachycardia, Ventricular Algorithms Deep Learning Electrocardiography Signal Processing, Computer-Assisted
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KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate From a Smartwatch.

IEEE transactions on bio-medical engineering Feb 20, 2025
Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they of...
Humans Algorithms Photoplethysmography Signal Processing, Computer-Assisted Heart Rate Male Deep Learning Wearable Electronic Devices
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Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach.

Computers in biology and medicine Feb 19, 2025
Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies; however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients...
Humans Neural Networks, Computer Female Male Deep Learning Parkinson Disease Signal Processing, Computer-Assisted Tremor
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Temporal and spatial self supervised learning methods for electrocardiograms.

Scientific reports Feb 19, 2025
The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, ...
Deep Learning Electrocardiography Signal Processing, Computer-Assisted Supervised Machine Learning Humans Algorithms Databases, Factual
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Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Computers in biology and medicine Feb 18, 2025
Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection can aid in early interventions and timely corrective actions. For this purpose, electroencephalogram...
Electroencephalography Signal Processing, Computer-Assisted Alcoholism Adult Male Middle Aged Machine Learning Female Humans Neural Networks, Computer
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A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

Scientific reports Feb 17, 2025
In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms h...
Male Deep Learning Electroencephalography Female Adult Epilepsy Signal Processing, Computer-Assisted Scalp Humans Neural Networks, Computer Algorithms
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