AIMC Topic: Signal Processing, Computer-Assisted

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A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.

Computers in biology and medicine
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
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
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
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
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
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)
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
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...

Variable Projection Support Vector Machines and Some Applications Using Adaptive Hermite Expansions.

International journal of neural systems
In this paper, we develop the so-called variable projection support vector machine (VP-SVM) algorithm that is a generalization of the classical SVM. In fact, the VP block serves as an automatic feature extractor to the SVM, which are trained simultan...

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.

Journal of healthcare engineering
Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular,...