A new AI neural network technique could help in identifying congestive heart failure (CHF) with 100% accuracy. The technique could predict the possibility of heart failure by looking at a single beat on the electrocardiograph. The use of neuroscience techniques is an integral part of medical research. However, fool-proof diagnosis through neural techniques is an unprecedented feat. The new technique, based on convolutional neural networks (CNN), could change that. This technique could be the first success of the medical fraternity in predicting heart failures with 100% accuracy.
Need for Improved Diagnosis
Congestive heart failure is a major cause of high mortality rate across several regions. The condition is characterized by extreme pain and discomfort. This is because the heart muscle loses its ability to pump blood in the event of a congestive heart failure. In addition, these factors necessitate the development of sound systems for preventing and diagnosing heart failure.
The new research was headed by Dr. Sebastiano Massaro from the University of Survey. The Biomedical Signal Processing and Control journal published the findings of the research. The research has paved way for improved diagnostic procedures for CHF. Current methods are based on heart-rate variability that increases the chances of error. Furthermore, these methods are obsolete and time-consuming for the medical staff.
Overcoming Limitations of Current Techniques
Current-day techniques for cardiac diagnosis require a detailed study of the electrocardiogram (ECG) report. The new CNN model uses machine learning tools and advanced signal processing to study ECG patterns. The use of these technologies ensures 100% accuracy of results. The researchers tested the CNN model on subjects with CHF, whilst considering their ECG dataset history. Additionally, the CNN model can help in gauging the severity of the condition.
Cardiologists are expected to embrace quick-diagnostic techniques without much ado. This propensity shall help in popularizing convolutional neural network models for the healthcare fraternity.