Materials-driven soft wearable bioelectronics for connected healthcare

S Gong, Y Lu, J Yin, A Levin, W Cheng - Chemical Reviews, 2024 - ACS Publications
In the era of Internet-of-things, many things can stay connected; however, biological
systems, including those necessary for human health, remain unable to stay connected to …

Heart rate variability for medical decision support systems: A review

O Faust, W Hong, HW Loh, S Xu, RS Tan… - Computers in biology …, 2022 - Elsevier
Abstract Heart Rate Variability (HRV) is a good predictor of human health because the heart
rhythm is modulated by a wide range of physiological processes. This statement embodies …

Electrocardiogram sampling frequency range acceptable for heart rate variability analysis

O Kwon, J Jeong, HB Kim, IH Kwon… - Healthcare …, 2018 - synapse.koreamed.org
Objectives Heart rate variability (HRV) has gained recognition as a noninvasive marker of
autonomic activity. HRV is considered a promising tool in various clinical scenarios. The …

Review of deep learning-based atrial fibrillation detection studies

F Murat, F Sadak, O Yildirim, M Talo, E Murat… - International journal of …, 2021 - mdpi.com
Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and
premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming …

Screening strategies for atrial fibrillation: a systematic review and cost-effectiveness analysis

NJ Welton, A McAleenan, HHZ Thom… - Health technology …, 2017 - discovery.ucl.ac.uk
BACKGROUND: Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk
of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been …

A review on the state of the art in atrial fibrillation detection enabled by machine learning

A Rizwan, A Zoha, IB Mabrouk… - IEEE reviews in …, 2020 - ieeexplore.ieee.org
Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the
main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally …

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal

F Hasanzadeh, M Mohebbi, R Rostami - Journal of affective disorders, 2019 - Elsevier
Background Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation
(rTMS) treatment is an important purpose that eliminates financial and psychological …

Integration of results from convolutional neural network in a support vector machine for the detection of atrial fibrillation

C Ma, S Wei, T Chen, J Zhong, Z Liu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Atrial fibrillation (AF) can cause a variety of heart diseases and its detection is insufficient in
outside hospital. We proposed three methods for AF diagnosis in ambulatory settings. The …

Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal

E Ebrahimzadeh, M Kalantari, M Joulani… - Computer methods and …, 2018 - Elsevier
Abstract Background and Objective Paroxysmal Atrial Fibrillation (PAF) is one of the most
common major cardiac arrhythmia. Unless treated timely, PAF might transform into …

Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection …

V Mazaheri, H Khodadadi - Expert Systems with Applications, 2020 - Elsevier
Cardiac arrhythmia disorder is known as one of the most common diseases in the world.
Today, this disease is considered as the leading cause of death in industrial and semi …