Researchers have used Artificial Intelligence(AI) to develop a neural network model that can accurately identify congestive heart failure with 100% accuracy through analysis of a single raw electrocardiogram (ECG) heartbeat.
Congestive heart failure i.e. CHF is a chronic progressive cardiac condition that affects the pumping ability of the heart muscles. As this is a highly prevalent complication that results in significant mortality rates and sustained healthcare costs, clinical practitioners and health organizations urgently require efficient detection processes.
Researchers focused on tackling these important concerns by means of Convolutional Neural Networks (CNN) which are hierarchical neural networks with high efficiency in recognizing patterns and structures in data.
Study researcher Sebastiano Massaro, Associate Professor at the University of Surrey in the UK, said, “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. 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”.
The research was published in the Biomedical Signal Processing and Control Journal and radically improves existing CHF detection methods that used to typically focus on heart rate variability that, whilst effective, were time-consuming and prone to errors.
In contrast, this new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% accuracy.
Study researcher Leandro Pecchia from the University of Warwick, “With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology”.