Certain investigation on performance analysis of modified hybrid parallel prefix adder for the adaptive filter design and ecg signal categorizing with machine learning models

Abstract

The Artificial Intelligence (AI) is one of the fast - growing fields. newlineMachine Learning (ML) is the subfield of AI. For the tracking of day to -day newlineactivities of the human body system the smart systems with bio medical newlinesignal processors are used. The current era of AI focused towards the newlinesimplified and multi connected devices. Any devices connected to the cloud newlinemust adopt the pruning changes and modify the characteristics and data. The newlinerecent surveys show that people have been affected by Cardio-Vascular (CV) newlinedisease. The irregular functionality of the heart is found 5 out of 10 people newlineunder 40 years. There are many factors are responsible for this, such as stress, newlinefood habit and culture so on. The prediction of the cardiovascular is important newlineone before its diagnosis at end stage. Therefore, the Electro Cardio Gram newline(ECG) are measured from human body and analysed for the CV diseases. The newlinediagnosis should be quicker, reliable and accurate. It is relied on the designing newlineof Computation Unit (CU) for ECG signal analysis to predict the CV disease newlineat most accurate rate. The main theme of this research is to design the most newlinesuitable CU to support the quicker and reliable process. newline