Estimation of Coherence among ECG and EEG Signals Using Various Auto-Regressive Methods. -

Introduction: An Electrocardiogram (ECG) is graphical tracing of electrical signals generated by heart muscles. An Electroencephalogram (EEG) is the electrical activity of brain. Coherence is degree of association between frequency spectra of two signals at a particular frequency. In this paper coherence between ECG and EEG signals of twelve different subjects is analysed by estimating magnitude squared coherence (MSC) between these two signals. ECG and EEG signals are taken simultaneously. Aim: Aim of this paper is to find the brain region at which maximum mean of MSC is exhibit. And out of four methods used which one is best suit for coherence estimation. Estimation of Coherence among physiological signals is used as cost-effective non-invasive tool of diagnostic. Coherence among ECG and EEG signals is used to determine the difference between normal and abnormal brain activity. Methods: Four different methods of power spectrum estimation are used for the analysis of MSC. These methods are Burg, Covariance, Modified Covariance and Welch method. Result: Maximum estimated magnitude squared coherence between ECG and EEG is at Cerebellum. It means that Cerebellum region of brain is maximum associated with heart. Out of four methods of power spectrum estimation such as Burg, Covariance, Modified-covariance and Welch, Burg method gives maximum MSC values. Conclusion: Magnitude squared coherence values among ECG and EEG signals of twelve subjects at four different brain regions are non-negative. It means that there is some association between heart and brain. Cerebellum region of brain has maximum association with heart. Burg method of power spectrum estimation is better than other methods used in this paper for the purpose of Coherence estimation.