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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

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

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing
CONCLUSION: PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.PMID:35144291 | DOI:10.1055/s-0042-1742671
Source: Methods of Information in Medicine - February 10, 2022 Category: Information Technology Authors: Alexander Gerharz Carmen Ruff Lucas Wirbka Felicitas Stoll Walter E Haefeli Andreas Groll Andreas D Meid Source Type: research