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Tors Doc detect Doc Doc new artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial neur

That's a recent study published in Biomedical Signal Processing and Control Journal, which explores how emerging technologies can improve existing methods of detecting congestive heart failure. & nbsp;

Led by researchers at the University of Surrey, Warwick and Florence, it shows that heartbeat.

CHF is a chronic progressive condition affecting the way in which blood is pumped around the body. Research shows that, in the US alone, around 5 million people live with it. & nbsp;

The researchers claim that they are "urgently requiring effective detection processes" as a result of "high prevalence, significant mortality rates and sustained healthcare costs". & Nbsp;

They believe that these questions can be solved through the use of convolutional neural networks (CNN), which are more effective for identifying patterns and structures in data. & Nbsp;

They are often time-consuming and inaccurate, their model combines advanced signal processing and machine learning tools on raw ECG signals.

Dr. Sebastiano Massaro, associate& nbsp; professor of organizational neuroscience at the University of Surrey, & nbsp;said: & nbsp; & nbsp;"First, by assessing ECG directly, we confirm that with it we are able to accurately detect and treat the disease."

In another part of the experiment, a specific CNN model has been used to improve the accuracy of the analysis of a comparable model. & Nbsp;

"We focus on the detection of the pathology of one heartbeat in excerpts of 5-minutes rather than 24-hours recordings," said Massaro. & Nbsp;

"This aspect offers a valuable potential for prospects of rapid interventions; It is also important to keep in mind that we are only talking about CHF patients at the moment. "

Going forward, Massaro hopes to extend the approach to broadcale samples and other classes of CHF so that the technology can ultimately be implemented in everyday healthcare systems and practices. & Nbsp;

He added: "The application of organizational neuroscience, and specifically for neural network approaches to healthcare issues.

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Tors Doc detect Doc Doc new artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial artificial neur

That's a recent study published in Biomedical Signal Processing and Control Journal, which explores how emerging technologies can improve existing methods of detecting congestive heart failure.

Led by researchers at the Universities of Surrey, Warwick and Florence, it shows that heartbeat.

CHF is a chronic progressive condition affecting the way in which blood is pumped around the body. Research shows that, in the US alone, around 5 million people live with it.

The researchers claim that this is an important requirement for high mortality, high mortality rates and high mortality rates.

They believe that these questions can be solved through the use of convolutional neural networks (CNN), which are more effective for identifying patterns and structures in data.

They are often time-consuming and inaccurate, their model combines advanced signal processing and machine learning tools on raw ECG signals.

Dr. Sebastiano Massaro, associate professor of organizational neuroscience at the University of Surrey, said: "First, by assessing ECG directly, we confirm that with it we are able to accurately detect and treat the disease."

In another part of the experiment, a specific CNN model has been used to improve the accuracy of the analysis of counterfeiting.

"We focus on the detection of the pathology of one heartbeat in excerpts of 5-minutes rather than 24-hours recordings," said Massaro.

"This aspect offers a valuable potential for prospects of rapid interventions; It is also important to keep in mind that we are only talking about CHF patients at the moment. "

Going forward, Massaro hopes to extend the approach to broadcale samples and other classes of CHF so that the technology can be implemented in everyday healthcare systems and practices.

He added: "The application of organizational neuroscience, and specifically for neural network approaches to healthcare issues.