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Effective Connectivity Between Nervous System and Cardiovascular System by Time Series Extracted from ECG signal and Frequency Bands of EEG signal

Shamsaei, Bahar | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57831 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Shamsollahi, Mohammd Bagher
  7. Abstract:
  8. There is an effective and functional connectivity between the central nervous system and the cardiovascular system, which is controlled by the autonomic nervous system. Investigating this connectivity and its direction can provide valuable insights into their functioning. To examine causal and directional relationships, EEG and ECG signals, which contain useful information about the activities of these two systems, are utilized. Granger causality is a popular method for estimating effective linear connectivity. However, the assumption of linearity in effective connectivity is not always valid, as nonlinear interactions also exist between the nervous and cardiovascular systems. Estimating effective nonlinear connectivity using Granger causality requires incorporating a nonlinear function into its formulation. Previous studies investigating these interactions typically employed HRV time series due to their relationship with the autonomic nervous system and EEG frequency bands. The use of other time series derived from ECG signals to explore the interactions between the nervous and cardiovascular systems has not yet been established. In this research, to investigate the interaction between these systems, in addition to HRV time series, the RAMP and QRSd time series derived from ECG signals were also utilized. The RAMP time series is extracted from the R-wave amplitude relative to the baseline, while the QRSd time series is obtained from the duration of each QRS complex. Furthermore, time series corresponding to EEG frequency bands were included in the analysis. The study employed the DREAMER database, which pertains to emotional states. For estimating linear effective connectivity, the Granger causality approach, based on the AR model, was applied. Subsequently, using the LSTM neural network as a nonlinear function, an NAR model was constructed to examine nonlinear effective connectivity. Linear and nonlinear causal interactions were calculated for three groups of emotions with positive, negative, and neutral arousal. The results across these groups revealed bidirectional linear and nonlinear interactions between the two systems. Additionally, it was shown that the RAMP and QRSd time series could provide unique interactions compared to the RR time series. In some pairs of time series, the interactions were bidirectional. Furthermore, in certain interactions, the RAMP and QRSd time series behaved similarly to the RR time series
  9. Keywords:
  10. Effective Connectivity ; Cardiovascular System ; Granger Causality ; Long Short Term Memory (LSTM) ; Electrocardiogram (ECG)Modeling ; Nervous System ; Electroencphalogram Signal

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