Dynamic energy consumption using multiobjective genetic algorithm based FFT for implantable cardiac pacemakers

Abstract

The development of miniature electronic devices that are implanted directly in heart is made possible by advancements in enrichment of high-density power electronic technologies. It include pacing devices to maintain normal heart rates, long-term rhythm analysis tools for detecting arrhythmias in cases of unexplained syncope, and heart failure tools that allow for real-time monitoring of cardiac pressures to identify and warn against early fluid overload. This paper proposes, Improved Notch Filter to mitigate noise contained by the recorded electrocardiogram (ECG) signal. Fast Fourier Transform (FFT) spectrum analysis approach, which is critical in embedded biomedical applications is engaged to detect patterns. To achieve low power operation, FFT-based R-peak signal processing is typically computed by Application Specific Integrated Circuits in deeply integrated systems such as cardiac pacemakers. The entire operating life cycle of pacemaker adopts FFT based feature extraction and classification which in turn consumes a considerable part of energy. This insists on the need for power optimization in FFT algorithm and hence a meta-heuristic multi-objective Genetic Algorithm is incorporated with FFT for improving arithmetic computation efficiency. The proposed approach considers the inherent spatial properties of ECG signals for efficient generation of frequency spectrum by FFT and also the number of execution cycles gets reduced. The framework is examined using MATLAB and the generated results obtained reveal that, the suggested work enables improved battery life for the cardiac pacemaker with reduced dynamic power consumption.