Pollution is a major problem all over the world. According to the energy conservation statement law states that energy cannot be created or destroyed but can be converted from one form to another. As it relates to nature, the sudden increase in urban and industrial growth has been completed with potentially hazardous waste. Sound is the electrical energy of the machine and can be converted into electrical energy through many provocative methods including heating using piezoelectric material and diaphragm. Other sounds are not preventable such as road noise, market noise, industry, train stations etc. and those unnecessary noises can be used to generate electricity. In this way we can reduce the use of piezoelectric power and energy generated by chemical materials where it is converted directly from energy to electrical energy. The widespread use of this space is to focus on how we can increase the electrical performance generated by the conversion of sound energy from non-renewable sources. When sound vibrations are in the diaphragm and are compressed and hard to convert into electrical energy.
During the last few years, a lot of study and research on the analysis of Electrocardiogram(ECG) has provided an intensive approach for the diagnosis of various heart diseases which is a leading cause of death worldwide. By examining the ECG waveform it is possible to obtain a number of useful measurements. The most important of these is the “QT interval” measurement because an abnormal QT interval can be allied with ventricular arrhythmias and sudden cardiac death. The QT interval is obtained by the detection of onset of the QRS complex and offset of the T wave. This work includes an endeavor for the efficient measurement of QT interval which mainly comprises of five stages like finding R peak using Pan Tompkins algorithm, detection of QRS onset, detection of T wave offset, measurement of QT interval and QT interval correction. The measurement of QT interval is implemented using LabWindows/CVI (C for Virtual Instrumentation). The performance evaluation of R peak detection is tested as per AAMI/ANSI/IEC 60601-2-47 standards using the databases that are available in Physionet like QT Database and MIT-BIH Arrhythmia Database. The performance evaluation of QT interval measurement is tested using a MindrayuMEC 10 Multiparametric monitoring unit and the algorithm achieved an accuracy of 96.73% for QT interval measurement.
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