Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100% accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat, a new study reports.
Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100% accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat, a new study reports.
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.
Dr Sebastiano Massaro, Associate Professor of Organisational Neuroscience at the University of Surrey, has worked with colleagues Mihaela Porumb and Dr Leandro Pecchia at the University of Warwick and Ernesto Iadanza at the University of Florence, to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data.
Published in Biomedical Signal Processing and Control Journal, their 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.
Read more at University of Surrey
Photo Credit: PublicDomainPictures via Pixabay
