Congestive heart failure (CHF) is a condition in which the heart is unable to pump sufficiently to maintain blood flow to meet the needs of human body. In CHF, blood pumping power of heart muscles becomes weaker compared to that of normal heart muscles. Coronary artery diseases, record of past heart attack (myocardial infarction), hypertension, valvular heart disease are common causes of CHF. Modern day’s complicated lifestyle, poor dietary regime, lack of exercise, obesity, and smoking, have led to increasing susceptibility to this disease and is also regarded as one of the major cardiovascular disorders. In 2010, it was estimated that more than 23 million people worldwide have congestive heart failure. Thereby any method for prediction or alarm generation for CHF is of utmost importance. In this work, we have applied two Fractal Analysis techniques directly on the ECG waveforms obtained from subjects affected (with CHF) and normal subjects and finally come up with two parameters. The overall results show that for both the parameters there are significant quantitative differences between the diseased subjects and the normal subjects Increase in the values is an indicator of dysfunction of the heart and the extent of deviation is an indicator of the degree of dysfunction. The analysis leads to formulation of biomarkers which may be used to detect abnormalities of the human heart sufficiently ahead of the time before it turns out to be fatal. Eventually mobile apps may be prepared to compute the bio markers on a real time basis. The app will read data from an implanted loop recorder, compute and compare it with a benchmark preloaded for a healthy heart. Whenever the computed value overshoots/ falls below the preloaded value for healthy subjects by a pre-defined tolerance value, some audio visual markers will alert the individual of the impending cardiac disorder. The Hurst exponent analysis reflects the chaotic behavior of normal and CHF patients’ heart and the PSVG parameter can significantly differentiate the CHF patient from normal people.

Congestive heart failure (CHF) is a condition in which the heart is unable to pump sufficiently to maintain blood flow to meet the needs of human body. In CHF, blood pumping power of heart muscles becomes weaker compared to that of normal heart muscles. Coronary artery diseases, record of past heart attack (myocardial infarction), hypertension, valvular heart disease are common causes of CHF. Modern day’s complicated lifestyle, poor dietary regime, lack of exercise, obesity, and smoking, have led to increasing susceptibility to this disease and is also regarded as one of the major cardiovascular disorders. In 2010, it was estimated that more than 23 million people worldwide have congestive heart failure. Thereby any method for prediction or alarm generation for CHF is of utmost importance. In this work, we have applied two Fractal Analysis techniques directly on the ECG waveforms obtained from subjects affected (with CHF) and normal subjects and finally come up with two parameters. The overall results show that for both the parameters there are significant quantitative differences between the diseased subjects and the normal subjects Increase in the values is an indicator of dysfunction of the heart and the extent of deviation is an indicator of the degree of dysfunction. The analysis leads to formulation of biomarkers which may be used to detect abnormalities of the human heart sufficiently ahead of the time before it turns out to be fatal. Eventually mobile apps may be prepared to compute the bio markers on a real time basis. The app will read data from an implanted loop recorder, compute and compare it with a benchmark preloaded for a healthy heart. Whenever the computed value overshoots/ falls below the preloaded value for healthy subjects by a pre-defined tolerance value, some audio visual markers will alert the individual of the impending cardiac disorder. The Hurst exponent analysis reflects the chaotic behavior of normal and CHF patients’ heart and the PSVG parameter can significantly differentiate the CHF patient from normal people.


Authors: Rajib Sarkar, Anirban Bhaduri, Susmita Bhaduri and Dipak Ghosh
Chemical informatics DOI: 10.21767/2470-6973.100018
Chem Inform. 2016, 2:2
Status :Published
Area : Biomedical Data analysis