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Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: Evaluation of a machine learning risk prediction algorithm.
Conclusions: Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources. PMID: 31855091 [PubMed - as supplied by publisher]
Source: Journal of Medical Economics - December 20, 2019 Category: Health Management Tags: J Med Econ Source Type: research

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation
Objective : Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as continuous long-term monitoring of heart arrhythmias, fitness, and sleep tracking, and hydration monitoring. One major issue that can hinder PPG-based applications is movement artifacts, which can lead to false interpretations. In many implementations, noisy PPG signals are discarded. Misinterpreted or discarded PPG signals pose a problem in applications where the goal i...
Source: Physiological Measurement - December 26, 2019 Category: Physiology Authors: Tania Pereira, Cheng Ding, Kais Gadhoumi, Nate Tran, Rene A Colorado, Karl Meisel and Xiao Hu Source Type: research

Sensors, Vol. 20, Pages 2136: Detection of Atrial Fibrillation Using 1D Convolutional Neural Network
a Hsiao The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detectio...
Source: Sensors - April 9, 2020 Category: Biotechnology Authors: Chaur-Heh Hsieh Yan-Shuo Li Bor-Jiunn Hwang Ching-Hua Hsiao Tags: Article Source Type: research

A stacked LSTM for atrial fibrillation prediction based on multivariate ECGs
AbstractAtrial fibrillation (AF) is an irregular and rapid heart rate that can increase the risk of various heart-related complications, such as the stroke and the heart failure. Electrocardiography (ECG) is widely used to monitor the health of heart disease patients. It can dramatically improve the health and the survival rate of heart disease patients by accurately predicting the AFs in an ECG. Most of the existing researches focus on the AF detection, but few of them explore the AF prediction. In this paper, we develop a recurrent neural network (RNN) composed of stacked LSTMs for AF prediction, which called SLAP. This ...
Source: Health Information Science and Systems - April 20, 2020 Category: Information Technology Source Type: research

Integrating the STOP-BANG score and clinical data to predict cardiovascular events after infarction: A machine learning study.
Abstract BACKGROUND: Obstructive sleep apnea (OSA) conveys worse clinical outcomes in coronary artery disease patients. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores obtained during the management of myocardial infarction (MI) patients. Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION: Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who suffered an in-hospital cardiovascular event ...
Source: Chest - April 24, 2020 Category: Respiratory Medicine Authors: Calvillo-Argüelles O, Sierra-Fernández CR, Padilla-Ibarra J, Rodriguez-Zanella H, Balderas-Muñoz K, Arias-Mendoza MA, Martínez-Sánchez C, Selmen-Chattaj S, Dominguez-Mendez BE, van der Harst P, Juarez-Orozco LE Tags: Chest Source Type: research

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network
AbstractAtrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in t...
Source: Journal of Medical Systems - May 9, 2020 Category: Information Technology Source Type: research

Early and mid-term outcomes of minimally invasive mitral valve repair via right mini-thoracotomy: 5-year experience with 129 consecutive patients
ConclusionsEarly and mid-term results of MIMVr were satisfactory, with low rates of perioperative morbidity and recurrent MR, as well as reoperation and death. Furthermore, the protocols for patient selection and surgical approach were considered to be appropriate.
Source: General Thoracic and Cardiovascular Surgery - January 5, 2021 Category: Cardiovascular & Thoracic Surgery Source Type: research

Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events
ConclusionVisualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.
Source: Frontiers in Physiology - February 18, 2021 Category: Physiology Source Type: research

Establishing minimally invasive cardiac surgery in a low-volume mitral surgery centre
DISCUSSION: It is possible for low-volume cardiac surgical centres to undertake minimally invasive surgical programmes with good outcomes and short learning curves. Despite technical complexities, with a team approach, the learning curve can be navigated safely.PMID:34058117 | DOI:10.1308/rcsann.2020.7092
Source: Annals of the Royal College of Surgeons of England - May 31, 2021 Category: Surgery Authors: B H Kirmani A Knowles P Saravanan J Zacharias Source Type: research

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