Heart rate and heart rate variability measures of cardio-vascular health: Study
27 July 2010
According to research cited in the International Journal of Electronic Healthcare, a statistical analysis of publicly available heart rate data using three classification tools -- Random Forests, Logistic Model Tree and Neural Network could help accurate diagnosis of heart problems.
According to C Vimal and colleagues at the PSG College of Technology, Coimbatore, heart rate and heart rate variability (HRV) are important measures that could indicate an individual's state of cardio-vascular health.
HRV analysis has gained importance in the field of cardiology for the detection of cardiac abnormalities.
Vimal and his team in the Department of Biomedical Engineering in association with V Mahesh in the Department of Information Technology investigated whether or not it would be possible to more quickly detect heart problems and possible indicators of imminent heart failure more quickly than using current techniques.
According to the team, automated detection and classification of cardiac diseases could help the physician in speedy diagnosis of cardiac abnormalities. They say the starting point of any study is usually an Electrocardiogram (ECG), a basic and widely used non-invasive diagnostic tool. The ECG records the heart's electrical activity.
However, the ECG has a major drawback as the heart's behavior can be inconsistent and symptoms of disease may show up at any time. The monitoring of the heart rate variability signal over a long period can be extremely time-consuming but at the same time can be productive in detecting abnormalities.