enhancement of sparse spasmodic sampling using novel machine learning fusion technique

This research explores the demands of compressive sensing (cs) and machine learning (ml) in biomedical signal processing. the sparse spasmodic sampling (sss) technique has gained significant attention in compressive sensing. the sss samples the signal irregularly and spasmodically. combining machine learning (ml) with sparse spasmodic sampling (sss) enhances accuracy and improves anomaly detection in biomedical signals. we propose a machine learning-based novel fusion technique that enhances sparse spasmodic sampling (ml-sss). mathematical analysis, extensive simulations, and experimental results show notable improvements in reconstruction accuracy and precision. the reconstruction using the proposed model achieves a high signal-to-noise ratio (snr) of up to 42 db at a high compression factor of 10%. the achieved accuracy is approximately 95%, and the precision is about 93.3% when detecting abnormalities. this approach paves the way for advanced applications in signal processing and medical imaging, where efficient data acquisition and processing are critical. the proposed framework offers a promising direction for bridging the gap between compressive sensing and intelligent algorithms in anomaly detection.