Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 percent 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.
This research drastically improves existing CHF detection methods typically focused on heart rate variability that are time-consuming and prone to errors. Their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 percent accuracy. Researchers trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Models delivered 100 percent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure.