AIMC Topic: Signal Processing, Computer-Assisted

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Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.

Computers in biology and medicine
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...

An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.

Computers in biology and medicine
Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals with epilepsy. Over recent years, a multitude of deep learning-based approaches have emerged to tackle this challenging task, leading to significant ad...

Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor.

Computers in biology and medicine
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal p...

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Neural networks : the official journal of the International Neural Network Society
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which ...

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. H...

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

PloS one
With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of ...

GloGen: PPG prompts for few-shot transfer learning in blood pressure estimation.

Computers in biology and medicine
With the rapid advancements in machine learning, its applications in the medical field have garnered increasing interest, particularly in non-invasive health monitoring methods. Blood pressure (BP) estimation using Photoplethysmogram (PPG) signals pr...

Correlation Fuzzy measure of multivariate time series for signature recognition.

PloS one
Distinguishing different time series, which is determinant or stochastic, is an important task in signal processing. In this work, a correlation measure constructs Correlation Fuzzy Entropy (CFE) to discriminate Chaos and stochastic series. It can be...

ECG classification based on guided attention mechanism.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Integrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specif...

Protocol for UAV fault diagnosis using signal processing and machine learning.

STAR protocols
Unmanned aerial vehicles (UAVs) require fault diagnosis for safe operation. Here, we present a protocol for UAV fault diagnosis using signal processing and artificial intelligence. We describe steps for collecting vibration-based signal data, preproc...