Development of the ‘Healthcor’ System as a Cardiac Disorders Symptoms Detector using an Expert System based on Arduino Uno

Abstract

In the modern era, our lifestyles are very fast-moving; this makes us highly susceptible to diseases, especially those associated with heart problems. In this research, we developed a portable early detection system for cardiac disorders. This system consists of passive electrodes, named SHIELD-EKG-EMG-PA; a shield which allows Arduino-like boards to capture electrocardiography (ECG) and electromyography (EMG) signals, named SHIELD-EKG-EMG, both devices produced by Olimex; a microcontroller, based on Arduino Uno; and an expert system which is implemented by a personal computer. This system detects time intervals of various segments in ECG signals which are captured by the devices; it then analyzes the signals in order to determine whether the patient has cardiac disorders. We call our detecting system the HEALTHCOR system. A database was established, containing various possible values of parameters in ECG signals. The types of diseases that can be detected are heart rhythm disorders including sinus bradycardia, sinus tachycardia, sinus arrhythmia, and cardiac symptoms associated with intervals and the wave height, such as myocardial infarction. From our tests, the accuracy of our system is 96%. The resultant diagnoses of four patients are all appropriate, and used a commercial 12-lead electrocardiograph.

Feel the Time. Time Perception as a Function of Interoceptive Processing

“…An IAc score was established with the Schandry heart beat task (Schandry, 1981 ) through a portable ECG unit sampling at 250 Hz (Villarrubia et al, 2014a , b ; Stojanović et al, 2015 ; Ševcík et al, 2015 ; Hugeng and Kurniawan, 2016 ) with Ag/AgCl electrodes. Time intervals were 25, 35, 45 and 100 s. Accuracy index was calculated with the following formula: 1/4∑(1 − (|recorded heartbeats − counted heartbeats|)/recorded heartbeats).…”

Design of Electrolarynx to Increase Energy Efficiency by Varying the Driving Source

“…The experimental setup is shown in Figure 5. The waveform was stored in an Arduino microcontroller Atmega328P as a lookup table in binary format (Hugeng and Kurniawan, 2016). We programed the microcontroller to generate an analog signal at a frequency of 100 Hz.…”

Measuring Biosignals with Single Circuit Boards

“…It should be mentioned that while some research groups have concentrated on developing and testing electronics especially suitable for ECG measurement in combination with Arduino or other single circuit boards, other research groups have gone further and aimed at developing a classification of the measured ECG data, e.g., by artificial intelligence and different neural networks [ 43 , 81 , 82 ].…”