New AI detects heart failure with 100% accuracy
With the help pf Artificial Intelligence (AI), researchers have developed a neural network developed that can accurately identify congestive heart failure with 100% 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)- hierarchial 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% accuracy by checking just one heartbeat we are able to detect whether or not a person has heart failure, said the researcher.
The Prof. said, our model is also one of the 1st known to be able to identify the ECG's morphological features specifically associated to the severity of the condition.
The research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors. Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% 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 the researcher.
this is only for your information, kindly take the advice of your doctor for medicines, exercises and so on.
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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)- hierarchial 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% accuracy by checking just one heartbeat we are able to detect whether or not a person has heart failure, said the researcher.
The Prof. said, our model is also one of the 1st known to be able to identify the ECG's morphological features specifically associated to the severity of the condition.
The research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors. Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% 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 the researcher.
this is only for your information, kindly take the advice of your doctor for medicines, exercises and so on.
https://gscrochetdesigns.blogspot.com. one can see my crochet creations
https://gseasyrecipes.blogspot.com. feel free to view for easy, simple and healthy recipes
https://kneereplacement-stickclub.blogspot.com. for info on knee replacement
With the help of Artificial Intelligence(AI), researchers ha ..
Labels: accurately, affects, analysis, artificial intelligence(AI), chronic, condition, congestive heart failure, ECG, heart muscles, identify, neural networks, power, progressive, pumping
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