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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

Cardiac risk stratification in cancer patients: A longitudinal patient –patient network analysis
by Yuan Hou, Yadi Zhou, Muzna Hussain, G. Thomas Budd, Wai Hong Wilson Tang, James Abraham, Bo Xu, Chirag Shah, Rohit Moudgil, Zoran Popovic, Chris Watson, Leslie Cho, Mina Chung, Mohamed Kanj, Samir Kapadia, Brian Griffin, Lars Svensson, Patrick Collier, Feixiong Cheng BackgroundCardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy –related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited k...
Source: PLoS Medicine - August 2, 2021 Category: Internal Medicine Authors: Yuan Hou Source Type: research

Sensors, Vol. 21, Pages 5302: Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques
uang Zhou Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection ...
Source: Sensors - August 5, 2021 Category: Biotechnology Authors: Yaru Yue Chengdong Chen Pengkun Liu Ying Xing Xiaoguang Zhou Tags: Article Source Type: research

Investigation on Imaging Features and Clinical Significance of Cardiac CT in Comprehensive Evaluation of Aortic Valve and Root before Percutaneous Aortic Valve Replacement
This study retrospectively includes patients with severe aortic stenosis who underwent TAVR with routine computed tomography. Based on CT images, the determination and grouping of bicuspid aortic valve and tricuspid aortic valve were completed. Thirteen cross-sectional levels of the aorta-iliac-femoral vascular access were completed. The results showed that 3 people had stroke (17.6%) and 5 people had myocardial infarction (29.4%) during the follow-up period. Atrial fibrillation occurred in 5 patients (29.4%), permanent pacemaker implantation was performed in 1 patient (5.9%), and acute kidney injury occurred in 7 patients...
Source: Biomed Res - September 30, 2022 Category: Research Authors: Xiong Tan Juan Peng Source Type: research

MRI-based training model for left atrial appendage closure
ConclusionThe proposed contrast-agent and radiation-free MRI-based training model for percutaneous LAA closure enables the pre-interventional assessment of the influence of the TSP site on the access of patient-specific LAA shapes. A straightforward replication of this work is measured by using clinically available imaging protocols and a widespread 3D printer technique to build the model.
Source: International Journal of Computer Assisted Radiology and Surgery - March 30, 2023 Category: Intensive Care Source Type: research

Cardiovascular disease (CVD) outcomes and associated risk factors in a medicare population without prior CVD history: an analysis using statistical and machine learning algorithms
AbstractThere is limited information on predicting incident cardiovascular outcomes among high- to very high-risk populations such as the elderly ( ≥ 65 years) in the absence of prior cardiovascular disease and the presence of non-cardiovascular multi-morbidity. We hypothesized that statistical/machine learning modeling can improve risk prediction, thus helping inform care management strategies. We defined a population from the Medicare he alth plan, a US government-funded program mostly for the elderly and varied levels of non-cardiovascular multi-morbidity. Participants were screened for cardiovascular disease (CVD)...
Source: Internal and Emergency Medicine - June 9, 2023 Category: Emergency Medicine Source Type: research

Sensors, Vol. 23, Pages 5618: Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
s D. Zink Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insu...
Source: Sensors - June 15, 2023 Category: Biotechnology Authors: Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink Tags: Article Source Type: research

Prediction of short-term atrial fibrillation risk using primary care electronic health records
Conclusions FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.
Source: Heart - June 26, 2023 Category: Cardiology Authors: Nadarajah, R., Wu, J., Hogg, D., Raveendra, K., Nakao, Y. M., Nakao, K., Arbel, R., Haim, M., Zahger, D., Parry, J., Bates, C., Cowan, C., Gale, C. P. Tags: Open access, Editor's choice Arrhythmias and sudden death Source Type: research

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