i am working for prediction of casualties by heart diseases through ECG signals (HRV analysis). it would be helpful if experts in this field suggest for what kind of HRV changes are observed for any abnormal activity by the heart.
I am afraid that this is an impossible task with using one single parameter autonomic dysfunction - HRT only. To my mind, it is applied strictly to screening. The predictive value of HRV may be relevant only in subjects with high Framingham risk or diabetes.
A reduction of HRV has been reported in several cardiovascular (Myocardial infarction,Diabetic neuropathy,Cardiac transplantation,Myocardial dysfunction - Heart Failure) and noncardiovascular diseases (Tetraplegia,Sepsis,Liver cirrhosis).
Initially, the focus of HRV investigation was its use in the prediction of long-term survival in patients who had suffered myocardial infarction, or had valvular or congestive heart disease. More recently, work has concentrated on attempts to predict the timing of onset of fatal ventricular tachyarrhythmias (VTAs). While the prognostic value of HRV post-myocardial infarction is well established, evidence of value in VTAs and sudden death is less clear. It is not yet possible to predict the onset of ventricular arrhythmias using HRV techniques, there is now the best predictors of VTAs include TWA, HRT, QRS duration, fragmented QRS, and QRS-T angle parameters. As shown by many studies, autonomic dysfunction may be a less important or variable factor in the pathophysiology of ventricular arrhythmias in noncoronary disease, therein TWA was the most sensitive predictor. In contrast, HRV,HRT, and BRS were not statistically significant predictors.Some studies show that reduced HRV is predictive for obstructive coronary artery disease independent of traditional risk factors and Framingham risk.
In support and in addition to the answer of Dr. Tatiyana Vaikhanskaya;
During last two years, we have made a pilot research about HRV. The idea came during one of the modules at our Uni. Participants (students) were obliged to detect biological signals and use them as a core for human-computer interface.
Few groups worked on HRV. Even though we have used higher order statistics, the main outcome was poor. For instance, by comparing an average person and a sportsman or long-term smoker, we have found the HRV of those different in a very small details - mostly not sufficient to make a clear differentiation between subjects. Usage of simple and fast algorithms seems to be insufficient.
I would propose to read also some of the papers mentioned below - maybe they will bring some ideas:
-->Gać P, et al. Effects of cigarette smoke on Holter ECG recordingsin patients with arterial hypertension. Part 1: Timedomain parameters of heart rate variability. En v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 7 ( 2 0 1 4 ) 404–413
-->Mirmohamadsadeghi L, et al. Respiratory rate estimation from the ECG using an instantaneousfrequency tracking algorithm.Biomedical Signal Processing and Control 14 (2014) 66–72
--> ChuDuc H, et al. A Review of Heart Rate Variability and its Applications. APCBEE Procedia 7 ( 2013 ) 80 – 85.
Would be nice to hear about you own observations, in some time.
HRV does not allow the diagnosis of any disease. HRV has limited predictive capabilities. It is much better to think about the HRV, as a method for investigation of sinus node physiology. When someone is interested in the mechanisms HRV, he will have to learn a lot of related issues, such as baroreflex, the extracellular matrix of the heart, synaptic transmission, etc. The researcher, who painstakingly examines the sinus node anatomy, its autonomic innervation and molecular machinery of cellular pacemaking, can much better understand nature of cardiac arrhythmias. And not only the arrhythmias but also many other diseases. That's why I love HRV, for the breadth of physiological concepts!