Leveraging asynchronous quantum secretary bird Generative propagation adversarial attention networks for FIR filter design in ECG applications

The escalating demand for better ECG signal analysis has created a demand for designs of better filters. This paper provides an alternate methodology towards proposing filter designs, through the use of Asynchronous Quantum Secretary Bird Generative Propagation Adversarial Attention Networks (Asyn-Qan-SBG-P2AN). In this proposal, the optimal filter coefficients are derived through the employment of Quantum Generative Adversarial Networks (QGAN), filter response characteristics are derived using Asynchronous Propagation Attention Networks (APAN) for adaptive signal feature extraction, and finally, using the Secretary Bird Optimization Algorithm (SBOA) based upon a filter's role, couples with Asyn-Qan-SBG-P2AN in differentiating ECG signals from other physiological measurements. Technology and method have combined to offer a generation of smart, adaptive and learning-based filter design in ECG applications. Our band-pass FIR filter has marked improvements in signal clarity, noise suppression, and resources being used to offer newly stunned opportunities for signal processing using lower specifications. Contributor offers a power consumption of 12 mW, Area 10 mm², Operating speed 300 MHz, and Frequency 4.8 GHz. The parameters also provide evidence that machine learning solutions can be used in real-time processing of ECG signals while capturing diagnostic AUCs, and provide a coronation for lower power possible in wearables.