Towards Virtual Instruments for Cardiovascular Healthcare: Real-time Modeling of Cardiovascular Dynamics using ECG Signals
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As cardiovascular disorders have emerged as the primary cause of human mortality, quantitative modeling of cardiovascular system, including the dynamic coupling among its chemical, electrical and pulmonary mechanisms, has evoked keen interest in the recent years. The current dynamic models have little clinical relevance because they cannot be correlated with the real-time signals from a human heart. This research presents a new approach for real-time simulation of the heart dynamics where the activation functions for the heart model are derived from the measured electrocardiogram (ECG) signal recording. The model is developed based on lumped mass nonlinear differential equations that can capture the coupled mechanical and physiological actions of the heart chambers, valves, pulmonary and systemic blood circulation loops. The model was implemented in Matlab/Simulink environment and tested using signals in MGH/MF Waveform Datasets from PhysioNet database. The results show that the model was able to discern the effect of variations in the electrical activations, as captured by ECG features on other heart signals, including the profiles of instantaneous pressure and volumes in different chambers and circulations. In specific, the time and frequency patterns of the right atrium and pulmonary blood pressure signals from the model closely match with the real measurements of arterial and pulmonary blood pressure, respectively. Furthermore, the model-derived pulmonary vein pressure matches with measured respiratory impedance signals. These findings support the suitability of developing a virtual instrument platform where the model-derived signals (presented appropriately) are used for clinical diagnostic in lieu of expensive instrumentations. In addition, the causal relationships between variations in ECG and the model outputs, such as pressure and volume signals, suggest definitive diagnosis methods for certain cardiovascular pathologies which are not easy to diagnosis from the ECG patterns. 2010 IEEE.
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2010 IEEE International Conference on Automation Science and Engineering
