Medical science is considered to take a leap, as, by means of Artificial Intelligence (AI), researchers have developed a strong neural network approach, that can accurately identify congestive heart failure with a guarantee of almost 100% accuracy, through analysis of just one rough electrocardiogram(ECG) heartbeat.
The main pumping power of the heart muscle is affected due to Congestive Heart Failure(CHF). Associated with the high prevalence, significant mortality rates, and sustained healthcare costs, clinical practitioners and health systems urgently require the procedure of efficient detection.
By means of Convolutional Neural Networks, the researchers have worked to tackle the necessary concerns. Hierarchical Neural Networks are highly effective in recognizing 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 percent accuracy: by checking just one heartbeat, we are able to detect whether or not a person has heart failure”, said the study researcher Sebastiano Massaro, Associate professor at the University of Survey in the UK.
“Our model is also one of the first known to be able to identify ECG’s morphological features associated to be the severity of the condition”, Massaro had said.
As it was published in the Biomedical Signal Processing and Control Journal, the research drastically improves existing CHF detection methods typically focussed on the rate of heart variability that whilst effective are time-consuming and prone to errors. The new model hence developed uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 percent accuracy.
“With approximate twenty-six million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology”, these words were said by the study researcher Leandro Pecchia, from the University of Warwick