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.

The authors trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection.

Published in Biomedical Signal Processing and Control Journal, the 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.

The author 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% 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."
 
https://www.surrey.ac.uk/news/new-ai-neural-network-approach-detects-heart-failure-single-heartbeat-100-accuracy

https://www.sciencedirect.com/science/article/pii/S1746809419301776?via%3Dihub

http://sciencemission.com/site/index.php?page=news&type=view&id=publications%2Fa-convolutional-neural&filter=22