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[HTML][HTML] Analysis of various techniques for ECG signal in healthcare, past, present, and future
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 …
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
Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of
death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac …
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
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 …
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
Deep learning plays a vital role in classifying different arrhythmias using
electrocardiography (ECG) data. Nevertheless, training deep learning models normally …
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
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …
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
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 …
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 …
Abstract This Committee Report provides methodological, interpretive, and reporting
guidance for researchers who use measures of heart rate (HR) and heart rate variability …
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
Healthcare optimization has become increasingly important in the current era, where
numerous challenges are posed by population ageing phenomena and the demand for …
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
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 …
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
Automated electrocardiogram (ECG) classification using machine learning (ML) is
extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically …
extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically …