Health problems with cardiovascular system disorders are still ranked high, according to data from the WHO reported that there are about 31% of causes of death globally are cardiovascular diseases. The purpose of this study was to develop a 12 lead electrocardiograph with 2 displays and the HC-05 as a real-time transmitter of heart signal data. The electrocardiogram signal is obtained from the wiretapping by attaching the electrode cable to the Lead I, Lead II, Lead III, aVR, aVL, and aVF leads, then processed on IC AD620, HPF and LPF filters and non-inverting amplifiers and then processed using Arduino UNO for further display. in the form of a signal on the Delphi 7 application. The research method is to measure the heart signal on the ECG Simulator, by testing several BPMs, namely 30, 60, 120 and 240 on each lead. After testing the signal equation at the 0.5mV setting by calculating the error rate, the highest error value is obtained in lead I, lead aVL and aVF of 7.14% and the smallest error is 3.57% error in lead III. Then at the 1mV setting by calculating the error rate, the highest error value in lead aVL is 7.14% and the smallest error is 2.36%. at the 2mV setting by calculating the error rate, the highest error value is obtained in leads aVL and aVF of 5.71% and the smallest error is obtained by an error of 2.1% in lead II. the results of this study are implemented so that in the future an ECG examination can be carried out and then monitored remotely like a doctor's room because the data communication uses bluetooth.
Brain lesions can cause severe dysfunction of the human body, and mind. It may result in fatality if it is not detected early. This paper focuses on the detection of lesions through image processing. Three different approaches are proposed to detect ischemic infarct. The detection of hemorrhagic infarct is done automatically with windowing DICOM images by setting optimal window width and window centre and then the lesion area is found by using human observation, database matching and saturation level detection method. This paper is mainly about way of aiding human to analyses lesion in CT Scan by finding the best windows setting and filter out the unnecessary information in DICOM images. The filtered images are applied with contrast enhancement to make observation and confirmation process easier and more accurate. The possible lesion areas are plotted with mark by human. After that the result is double confirmed with auto detection by computer.
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