Artificial Intelligence detects heart failure with 100% accuracy
With just one raw electrocardiogram (ECG) heartbeat, researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 per cent accuracy through analysis.
Chronic progressive condition is a Heart failure (CHF), that affects the pumping power of the heart muscles. with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.
Convolutional Neural Networks (CNN) – hierarchical neural Networks (CNN) – hierarchical neural networks highly effective in recognizing patterns and structures in data. Vice versa, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy.
They delivered 100 per cent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure. This 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.
It’s been estimated that, World widely, 26 million people affected by a form of heart failure, our research present a major advancement on the current methodology,” said study researcher Leandro Pecchia from the University of Warwick.