An Approach Towards Abnormal Heart Rate Variability Detection Through Pseudo-Shifter Enabled Neural Computing

The growing population, recent studies have highlighted a significant rise in cardiovascular attacks compared to previous years, reflecting an alarming trend that increases annually. This escalating prevalence underscores the urgent need for effective systems for cardiovascular disease detection and prevention. The proposed system addresses critical limitations in existing state-of-the-art approaches, particularly in achieving accurate predictions of heart abnormalities, which remain a common and persistent health threat. Leveraging a standard dataset, the system is designed to detect heart abnormalities in various patterns, ensuring comprehensive diagnostic coverage. Developed using advanced digital architecture, the system prioritizes high-speed data sampling and low-power operation, making it both efficient and scalable. A key innovation in this approach is the implementation of a low-power pseudo shifter-enabled pattern analysis algorithm (PAA), which enhances pattern detection while optimizing power consumption. The system achieved an impressive area coverage of just 1.23 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, reflecting its compact and efficient design. By employing an ideal mechanism to analyze and optimize inputs, the proposed platform represents a significant advancement in cardiovascular healthcare, offering a precise, energy-efficient, and scalable solution to address this growing medical challenge.