Correlation analysis between ECG and EEG signals based on signal complexity
Date Issued
2015
Date
2015
Author(s)
Lin, Pei-Feng
Abstract
Introduction The secret of life remains extremely concealed. There are all sorts of rhythms in human bodies and they are central to life. The rhythms interact with each other as well as the outside fluctuating, noisy environment under the control of innumerable feedback systems. They provide an orderly function that enables life. The heart has been considered the source of emotional experience and wisdom in many cultures throughout the ages. Most neuroscientists consider consciousness or even thought is merely an epiphenomenon of the human brain function and its associated neurophysiology. However, the heart begins to beat before the brain is formed. Conventionally, both neural and humoral pathways connect the heart with the brain. Whether the interplay between the heart and brain could be explored through their rhythms is the question. Heart rate variability is recognized as the indicator of cardiac autonomic function. The dynamics of human electroencephalography (EEG) dynamics has been proved to be related to cognitive activities. This dissertation starts with reviewing the nonlinear methods in analyzing biological rhythms, which are multiscale, nonlinear and non-stationary. Regardless of whether chaos is present, deterministic complexity exists in biological rhythms. Regularity based complexity was chosen after comparisons. The goal is to find correlations between EEG and electrocardiography (ECG) through regularity based complexity analysis. Both simultaneous and non-simultaneous data would be examined. The experimental subjects are from a geriatric sample with varied cognitive abilities and basically healthy hearts. The electromagnetic activity of the brain works at an extremely fast speed, and the quasi-stationary epochs of EEG are, in general, short lasting, in the order of tens of seconds. Therefore symbolic techniques were introduced when exploring the very short simultaneous EEG and R-R interval (RRI) data. The origin of EEG remains unknown. Slow cortical potential (SCP), one component of EEG, is in the frequency range similar to that of the heart, and would be explored in an intuitive nonlinear way. In addition, the amplitude and instantaneous frequency of EEG would be separately approached. Methods The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD), 22 fresh cases of Alzheimer’s disease (AD) and 29 controls. Multiscale entropy (MSE) analysis was applied to the non-simultaneous EEG and RRI data. Symbolic analysis was applied to the simultaneous EEG and RRI data. Discrete events (local peaks) of EEG were extracted to separate the amplitude and instantaneous frequency. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. Results and Discussions MSE revealed correlations between the signal complexity of brain and cardiac activities in non-simultaneous data. Linear correlation between the MSE value and the score of the mini-mental state examination was first found. Symbolic dynamics failed to correlate the heart to the brain. This is due to that the RRI is too short to represent the characteristics of a subject. The symbolic analysis revealed important information that the EEG dynamics which relates to either the cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons. Just like RRI of ECG, discrete events of EEG also provided important information. The relative value of complexity does not indicate health condition straightly. It depends on the method and the scale or dimension that particular method measures. Discrete events provide no less information than continuous waveforms of EEG. Pathological condition is continuous rather than stepwise.
Subjects
signal complexity
ECG
EEG
Multiscale entropy
symbolic dynamics
amplitude
instantaneous frequency
SDGs
Type
thesis
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