Seeing forest and trees could help detect heart problems

July 27th, 2010 - 5:49 pm ICT by ANI  

Washington, July 27 (ANI): Observing forests and trees could offer a rapid and precise way to diagnose heart problems, revealed a study by Indian researchers.

C. Vimal and colleagues at the PSG College of Technology, in Coimbatore, India. Found that a statistical analysis of publicly available heart rate data using three classification tools - Random Forests, Logistic Model Tree and Neural Network - could reveal heart problems

“Heart rate and Heart Rate Variability (HRV) are important measures that reflect the state of the cardiovascular system. HRV analysis has gained prominence in the field of cardiology for detecting cardiac abnormalities,” explained C. Vimal and colleagues at the PSG College of Technology, in Coimbatore, India.

The researchers investigated whether or not it might be possible to more quickly detect heart problems and possible indicators of imminent heart failure more quickly than with current techniques.

“Automated detection and classification of cardiac diseases can aid the physician in speedy diagnosis of cardiac abnormalities. The starting point of any study is usually an Electrocardiogram (ECG), which records the heart’s electrical activity,” explained the team.

The ECG is a basic but widely used non-invasive diagnostic tool.

Unfortunately, the ECG suffers from a major drawback, given that the heart’s behaviour can be inconsistent and symptoms of disease may show up at any time.

The heart rate variability signal monitored over a long period is more time-consuming but can be more productive in detecting abnormalities.

“The analysis of HRV data yields various features that have proved to be a better aid in classification,” explained the team.

Short-term variability in heart rate might be looked at or low and high frequency electrical changes.

Indeed, the low frequency/high frequency ratio has been found to be the most influential HRV determinant of death and could help to identify patients at risk, the team adds.

The team sourced heart data from various heart disease databases on the Physionet website, a site dedicated to medical data of various diseases and their study, and processed the signals using three different approaches: Random Forests, Logistic Model Tree and Multilayer Perceptron Neural Network - to validate the diagnostic conclusions.

The team was able to obtain a classification accuracy of 98.17 percent.

“The validated system can assist physicians in the classification of heart diseases. Future work will be aimed at studying the performance of the system using real-time patient data from hospitals to further validate the observations,” said the researchers.

The study has been published in the International Journal of Electronic Healthcare. (ANI)

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