AIMC Topic: Signal Processing, Computer-Assisted
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Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals.
Scientific reports
Feb 1, 2024
Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The devi...
Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.
Sensors (Basel, Switzerland)
Jan 29, 2024
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various appli...
A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.
Computers in biology and medicine
Jan 28, 2024
The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm over...
CLINet: A novel deep learning network for ECG signal classification.
Journal of electrocardiology
Jan 28, 2024
Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this pape...
The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.
IEEE transactions on biomedical circuits and systems
Jan 10, 2024
For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Art...
ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.
Medical engineering & physics
Jan 9, 2024
ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification...
EEG-BCI-based motor imagery classification using double attention convolutional network.
Computer methods in biomechanics and biomedical engineering
Jan 2, 2024
This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data,...
Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.
Computers in biology and medicine
Dec 29, 2023
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial i...
Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.
Sensors (Basel, Switzerland)
Dec 16, 2023
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor functio...
SEResUTer: a deep learning approach for accurate ECG signal delineation and atrial fibrillation detection.
Physiological measurement
Dec 15, 2023
Accurate detection of electrocardiogram (ECG) waveforms is crucial for computer-aided diagnosis of cardiac abnormalities. This study introduces SEResUTer, an enhanced deep learning model designed for ECG delineation and atrial fibrillation (AF) detec...
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