An efficient computation of discrete orthogonal moments for bio-signals reconstruction

Abstract Bio-signals are extensively used in diagnosing many diseases wearable devices. In signal processing, reconstruction is one of the essential applications. Discrete orthogonal moments (DOMs) effective analysis tools for signals that can extract digital information without redundancy. The propagation numerical errors a significant challenge computation DOMs at high orders. This problem damages orthogonality property these moments, which restricts ability to recover signal's distinct and unique components with no redundant information. paper proposes stable based on QR decomposition methods: Gram–Schmidt, Householder, Given Rotations methods. It also presents comparative study performance types moments: Tchebichef, Krawtchouk, Charlier, Hahn, Meixner moments. proposed algorithm's evaluation done using MIT-BIH arrhythmia dataset terms mean square error peak noise ratio. results demonstrate superiority method computing DOMs, especially moment Moreover, indicate Householder outperforms Gram–Schmidt methods execution time quality. show Charlier have superior quality than Hahn Tchebichef generally has highest reconstruction.