Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Authors

  • Kittimasak Naijit Chandrakasem Rajabhat University

DOI:

https://doi.org/10.3991/ijoe.v16i05.12707

Keywords:

emergency notification, combination algorithm, ECG signal

Abstract


Intensive Care Unit (ICU) Rooms usually have several detectors attached to each patient providing intensive care, and several processors control and interpret. If the processor detects an abnormality, the medical professional office will be alerted. Nevertheless, many patients with heart disease are concerned with day-to-day behaviors such as hard work, battle, exercise, shock, fight, and war. Become due to clinical depression and erectile impotence this induces anxiety and fear. The boundaries of your heart muscle and coronary strength are unclear. They want a warning that is quick and accurate before they lose control. We develop signal recognition for an algorithm that is very fast and accurate. It helps alert patients to avoid the operation of risk. Nevertheless, it is able to transfer information from the heartbeat network to the doctor's guidance system. The research would analyze the 200 signals from multiple ECG signals. Integration Component Diagnosis (ICD) is capable of extraordinary reliability of identification than the 17.10-41.93 average percentage of the Automata Matching Process which takes less time than the other 29.37 average percentage process and can alert within 12 seconds. It cannot be identified in a pattern other than without the detection of an ECG signal. In the experimental, the signal is used to distinguish 20 ECG signal patterns.

Author Biography

Kittimasak Naijit, Chandrakasem Rajabhat University

Department of Multimedia Technology, Faculty of Science

Downloads

Published

2020-05-14

How to Cite

Naijit, K. (2020). Emergency Notification Using Combination Algorithm with Recognition ECG Signal. International Journal of Online and Biomedical Engineering (iJOE), 16(05), pp. 15–30. https://doi.org/10.3991/ijoe.v16i05.12707

Issue

Section

Papers