Tuesday September 17, 2019

People Suffering from Insomnia Might have Increased Risk of Coronary Artery Disease, Heart Failure and Stroke

These observational studies were unable to determine whether insomnia is a cause, or if it is just associated with them

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Insomnia, Heart Disease, Heart Failure
According to researchers, previous observational studies have found an association between insomnia, which affects up to 30 per cent of the general population and an increased risk of developing heart disease and stroke. Pixabay

People suffering from insomnia might have an increased risk of coronary artery disease, heart failure and stroke, says a study.

According to researchers, previous observational studies have found an association between insomnia, which affects up to 30 per cent of the general population and an increased risk of developing heart disease and stroke.

“These observational studies were unable to determine whether insomnia is a cause, or if it is just associated with them,” said the study’s lead author Susanna Larsson, Associate Professor at the Karolinska Institute in Sweden.

In the study, the researchers applied Mendelian randomisation, a technique that uses genetic variants known to be connected with a potential risk factor, such as insomnia, to reduce bias in the results.

Insomnia, Heart Disease, Heart Failure
People suffering from insomnia might have an increased risk of coronary artery disease, heart failure and stroke, says a study. Pixabay

The 1.3 million participants with or without heart disease and stroke were drawn from four major public studies and groups, said the research published in the journal Circulation.

Researchers found genetic variants for insomnia were associated with significantly higher odds of coronary artery disease, heart failure and ischemic stroke – particularly large artery stroke.

“It is important to identify the underlying reason for insomnia and treat it.

“Sleep is a behaviour that can be changed by new habits and stress management,” Larsson said.

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A limitation to the study is that the results represent a genetic variant link to insomnia rather than insomnia itself. (IANS)

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Researchers Can Now Detect Heart Failure with 100% Accuracy with the Help of Artificial Intelligence

Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy

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Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

With the help of Artificial Intelligence(AI), researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 per cent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.

The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data.

“We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100 per cent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure,” said study researcher Sebastiano Massaro, Associate Professor at the University of Surrey in the UK.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“Our model is also one of the first known to be able to identify the ECG’ s morphological features specifically associated to the severity of the condition,” Massaro said.

Published in Biomedical Signal Processing and Control Journal, the research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors.

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Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy.

“With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology,” said study researcher Leandro Pecchia from the University of Warwick. (IANS)