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Tachycardia-Induced Cardiomyopathy As a Chronic Heart Failure Model in Swine

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Interval Type 2 Adaptive Neuro-Fuzzy Inference System-Based Artificial Pacemaker Design and Stability Analysis.

Asghar Dabiri Aghdam¹, Nader Jafarnia Dabanloo¹, Fereidoun Nooshiravan Rahatabad¹

¹Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Journal of Long-Term Effects of Medical Implants

|November 8, 2023

Summary

This study introduces an Interval Type 2 Fuzzy System (IT2FS) adaptive neuro-fuzzy inference system (ANFIS) pacemaker controller. The novel fuzzy C-means method significantly improves pacemaker performance metrics like rise-time and overshoot.

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Area of Science:

Biomedical Engineering
Control Systems
Artificial Intelligence

Background:

Previous research utilized Type 1 Fuzzy Inference Systems (T1FS) and Proportional Integral Derivative (PID) controllers for pacemakers, often employing least-square-estimation and backpropagation for tuning.
Analysis of Type 1 Fuzzy Logic (IT1FS) models in prior studies focused on step response, highlighting limitations in performance optimization.

Purpose of the Study:

To design and simulate an advanced Interval Type 2 Fuzzy System (IT2FS) based Adaptive Neuro-Fuzzy Inference System (ANFIS) for pacemaker control.
To enhance pacemaker controller performance by improving key time-domain metrics such as rise-time, overshoot, and settling-time.

Main Methods:

Development of an IT2FS-based ANFIS pacemaker controller using MATLAB.
Verification of system stability through time-domain analysis (unit step response).
Application of the Fuzzy C-Means (FCM) clustering method for tuning membership functions, outperforming traditional algorithms.

Main Results:

The designed IT2FS-ANFIS controller demonstrated significant improvements in rise-time, overshoot, and settling-time compared to previous models.
The FCM method proved more effective for membership function tuning than least-square-estimation and backpropagation algorithms used in prior work.
Stability of the designed fuzzy logic model was successfully verified in the time-domain.

Conclusions:

The IT2FS-ANFIS controller offers a superior approach to pacemaker design, providing enhanced patient heart rate regulation.
The FCM method represents a more effective strategy for optimizing fuzzy inference system parameters in biomedical control applications.
The proposed system achieves significant performance enhancements, paving the way for more sophisticated cardiac pacing solutions.