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Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review.
Sensors (Basel)
December 2024
REMIT (Research on Economics, Management and Information Technologies), IJP (Instituto Jurídico Portucalense), Universidade Portucalense, Rua Dr. António Bernardino de Almeida, 541-619, 4200-072 Porto, Portugal.
Some previous studies have focused on using physiological signals to detect stress in individuals with ASD through wearable devices, yet few have focused on how to design such solutions. Wearable technology may be a valuable tool to aid parents and caregivers in monitoring the emotional states of individuals with ASD who are at high risk of experiencing very stressful situations. However, effective wearable devices for individuals with ASD may need to differ from solutions for those without ASD.
View Article and Find Full Text PDFAccurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.
Sensors (Basel)
December 2024
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).
View Article and Find Full Text PDFArrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.
Sensors (Basel)
December 2024
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar.
View Article and Find Full Text PDFFused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption.
Sensors (Basel)
December 2024
School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China.
Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features.
View Article and Find Full Text PDFResearch on a Lightweight Arrhythmia Classification Model Based on Knowledge Distillation for Wearable Single-Lead ECG Monitoring Systems.
Sensors (Basel)
December 2024
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field.
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