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This neural network can identify heart failure with 100% accuracy

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Aakash Molpariya
Aakash started in Nov 2018 as a writer at Revyuh.com. Since joining, as writer, he is mainly responsible for Software, Science, programming, system administration and the Technology ecosystem, but due to his versatility he is used for everything possible. He writes about topics ranging from AI to hardware to games, stands in front of and behind the camera, creates creative product images and much more. He is a trained IT systems engineer and has studied computer science. By the way, he is enthusiastic about his own small projects in game development, hardware-handicraft, digital art, gaming and music. Email: aakash (at) revyuh (dot) com

The congestive heart failure (CHF) is a progressive chronic condition that affects the pumping power of the heart muscles. Now, thanks to the development of a neural network, congestive heart failure can be identified with 100% accuracy through the analysis of a single heartbeat without an electrocardiogram (ECG).

Just a beat

Published in the Biomedical Signal Processing and Control Journal by researchers at the University of Surrey, the results of this study suggest a drastic improvement of existing CHF detection methods, which generally focus on heart rate variability that, While it is effective, it consumes a lot of time and is prone to errors.

It is not what happens with this new approach, which makes zero errors, based on highly effective hierarchical neural networks to recognize patterns and structures in the data.

Specifically, the new model uses a combination of advanced signal processing and machine learning tools in raw ECG signals.

The model is also one of the first that can identify the morphological characteristics of the ECG specifically associated with the severity of the condition.

With approximately 26 million people worldwide affected by a form of heart failure, this research presents an important advance in the current methodology, allowing patients an early and more efficient diagnosis and thus relieving economic pressures on existing resources.

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