Transactions on Machine Intelligence

Transactions on Machine Intelligence

Modeling the Human Sinoatrial Node Based on Sequential Discharge Hypothesis

Document Type : Original Article

Authors
R. Yaghoobi Karimi 1
S. Azadi 1
O. Rahamni Seryasat 2
1 Department of Biomedical Engineering, Semnan University, Semnan, Iran
2 Department of Electrical Engineering, Faculty of Technology and Engineering, Adiban Institute of Higher Education, Garmsar, Iran.
10.47176/TMI.2020.43
Abstract
Achieving electrical synchronization among sinoatrial (SA) node cells, which is essential for initiating and propagating pacemaker activity, remains a complex and widely discussed issue in cardiac electrophysiology. While several previous studies have proposed theoretical models to explain this synchronization, the majority have been limited to small-scale simulations involving only a few interconnected SA node cells. To address this limitation, the present study introduces a novel computational model of the human SA node grounded in the sequential discharge hypothesis. This model features a three-dimensional (3D) lattice of cells governed by a modified Hodgkin-Huxley (HH) framework, where the membrane dynamics are adjusted using a time constant to simulate realistic propagation of action potentials through gap junctions. Simulation results demonstrate that the proposed model can produce stable oscillatory activity within a physiological heart rate range of 40 to 180 beats per minute (BPM). Furthermore, the 3D network effectively estimates the extracellular electrical potential in the vicinity of the SA node, closely matching the signals recorded by clinical electrodes. These findings suggest that the model offers a more comprehensive and scalable approach for studying the electrical synchronization mechanisms of large populations of SA node cells, particularly in human cardiac tissue.
Keywords
Large Mammalian
Human
Sinoatrial Node Model
Modified Hodgkin-Huxley Model
Pacemaker Activity
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Transactions on Machine Intelligence
Volume 3, Issue 1
Winter 2020
Pages 43-56

  • Receive Date 22 December 2019
  • Revise Date 15 February 2020
  • Accept Date 25 March 2020