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Source: Thailand Medical News  Sep 14, 2019  3 years ago
AI Neural Platform Detects Heart Failure From A Single Heartbeat.
AI Neural Platform Detects Heart Failure From A Single Heartbeat.
Source: Thailand Medical News  Sep 14, 2019  3 years ago
Researchers from University of Surrey have developed an AI based neural network platform that can identify congestive heart failure with 100 percent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat. Plans are underway to develop the platform so that it can be commercially used in the daily healthcare settings.

CHF or Congestive heart failure,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 to prevent further complications or deaths.

Associate Professor Dr. Sebastiano Massaro, from the department of 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 recognizing patterns and structures in data.

The team’s 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 percent accuracy.

"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 platform delivered 100 percent 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."commented Dr. Sebastiano Massaro in a phone interview with Thailand Medical News.

With approximately more than 400 million people worldwide affected by a form of heart failure each year, the implications of this development is phenomenal as will enable clinical practitioners to access an accurate CHF detection tool that can make a significant  healthcare impact, with patients benefiting from early and more efficient diagnosis and easing pressures on public healthcare worldwide. A commercial version of the AI based neural network platform is expected to be ready in less than three months.
Reference: Mihaela Porumb et al. A convolutional neural network approach to detect congestive heart failure, Biomedical Signal Processing and Control (2019). DOI: 10.1016/j.bspc.2019.101597


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