[HTML][HTML] Analysis of various techniques for ECG signal in healthcare, past, present, and future

T Anbalagan, MK Nath, D Vijayalakshmi… - Biomedical Engineering …, 2023‏ - Elsevier
Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …

Recent advances in cardiovascular disease biosensors and monitoring technologies

L Tang, J Yang, Y Wang, R Deng - ACS sensors, 2023‏ - ACS Publications
Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of
death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac …

Advanced meta-heuristics, convolutional neural networks, and feature selectors for efficient COVID-19 X-ray chest image classification

ESM El-Kenawy, S Mirjalili, A Ibrahim… - Ieee …, 2021‏ - ieeexplore.ieee.org
The chest X-ray is considered a significant clinical utility for basic examination and
diagnosis. The human lung area can be affected by various infections, such as bacteria and …

Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

A Raza, KP Tran, L Koehl, S Li - Knowledge-Based Systems, 2022‏ - Elsevier
Deep learning plays a vital role in classifying different arrhythmias using
electrocardiography (ECG) data. Nevertheless, training deep learning models normally …

[HTML][HTML] Estimating age and gender from electrocardiogram signals: a comprehensive review of the past decade

MY Ansari, M Qaraqe, F Charafeddine… - Artificial Intelligence in …, 2023‏ - Elsevier
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey

M Wasimuddin, K Elleithy, AS Abuzneid… - IEEE …, 2020‏ - ieeexplore.ieee.org
Electrocardiogram (ECG) gives essential information about different cardiac conditions of
the human heart. Its analysis has been the main objective among the research community to …

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology—Part 1: Physiological underpinnings and foundations of …

KS Quigley, PJ Gianaros, GJ Norman… - …, 2024‏ - Wiley Online Library
Abstract This Committee Report provides methodological, interpretive, and reporting
guidance for researchers who use measures of heart rate (HR) and heart rate variability …

Physiological and behavior monitoring systems for smart healthcare environments: A review

M Jacob Rodrigues, O Postolache, F Cercas - Sensors, 2020‏ - mdpi.com
Healthcare optimization has become increasingly important in the current era, where
numerous challenges are posed by population ageing phenomena and the demand for …

[HTML][HTML] A deep learning approach for atrial fibrillation classification using multi-feature time series data from ecg and ppg

B Aldughayfiq, F Ashfaq, NZ Jhanjhi, M Humayun - Diagnostics, 2023‏ - mdpi.com
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …

A tiny matched filter-based CNN for inter-patient ECG classification and arrhythmia detection at the edge

MM Farag - Sensors, 2023‏ - mdpi.com
Automated electrocardiogram (ECG) classification using machine learning (ML) is
extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically …