ECG generation methods for testing and maintenance of cardiac monitors

Ciucu, Radu; Serițan, George; Dragomir, Dan; Cepișcă, Costin; Adochiei, Felix-Constantin

doi:10.1109/ehb.2015.7391513

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…In general, the calculation of the heart rate (BPM) value starts from the interval between R and R, which indicates the period of heartbeat that can be converted into heart rate (H): H=6000/(R-R) (bpm), where R-R is the interval between peak signal R and peak signal R measured in milliseconds. The R-R interval is relatively constant from beat to beat, so the conversion of ECG signals can determine the BPM value [6]. The use of smartphone-based applications is very possible for patients and health workers to access heart health through the patient's ECG signal in real-time, such as it-based applications [7].…”

Section: Introductionmentioning

confidence: 99%

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

Utomo,

Triwiyanto,

Luthfiyah

et al. 2024

Bulletin EEI

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Information systems are currently developing very rapidly, and this is inseparable from the role of internet of things (IoT) technology, especially in the world of telemedicine. MitApp is an open-source application that can be used to monitor electrocardiogram (ECG) signals in real-time. The aim of this study is to develop an IoT-based ECG signal monitoring system that utilizes the MitApp application to detect abnormal ECG signals that are characterized by symptoms of cardiac arrhythmias. To process ECG signal data obtained from lead electrode results, the research method utilizes Arduino Uno as a microcontroller. The result is then displayed on the thin film transistor (TFT) layer using the Nextion module. The ESP32 module is used as a Wi-Fi module to send data to the MitApp app on a smartphone. The results showed that the results of the comparison test of ECG signal module data with ECG simulator tools with beats per minute values of 60, 80, 100, 120, and 140 obtained an error rate of 0.05. Based on these results, there is potential to develop this feature and integrate the system with the patient management system to improve the effectiveness of remote monitoring.

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Section: Introductionmentioning

confidence: 99%

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

Utomo,

Triwiyanto,

Luthfiyah

et al. 2024

Bulletin EEI

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“…In addition, the method of forming a heart signal on the DAC type MCP 4921 was also expected to be used. Therefore, it is expected that through this research, a learning model considering the importance of studying ECG signals and carrying out the functions of the ECG test kit can be developed [18].…”

Section: Introductionmentioning

confidence: 99%

ECG Simulator Based on Microcontroller Equipped with Arrhythmia Signal

et al. 2022

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Electrocardiograph (ECG) is one of the diagnostic sciences that is often studied in modern medicine, used to detect damage to the components of the heart or disorders of the heart rhythm called arrhythmias. The purpose of this research is to develop an Electrocardiograph simulator that is equipped with arrhythmia. The main design consists of an Arduino Mega 2560 microcontroller, MCP4921 DAC (Digital to Analog Converter) circuit, a network resistor, and a sensitivity selection circuit. The MCP4921 type DAC converts the digital signal data into analog data which will then be forwarded to the resistor network circuit as a signal formation for each lead. The basic signal image data used for the formation of normal Electrocardiograph and arrhythmias were taken from the Electrocardiograph recorder using Phantom Electrocardiograph. Based on the readings on the Beat Per Minute setting of the module to the Beat Per Minute printout on the Electrocardiograph recorder, the error rate value for the Normal Sine Rhythm parameter is 0.790% for Beat Per Minute 30, 0.383% for Beat Per Minute 60, 0.535% for Beat Per Minute 120, 0.515% for Beat Per Minute 180 and 0.593% for Beat Per Minute 240. The error rate for the Arrhythmia parameter is 2.076% for ventricular tachycardia Beat Per Minute 160 and 0.494% for Supraventricular Tachycardia Beat Per Minute 200. The design of the Electrocardiograph simulator can simulate the signals of the human body and it can be used as a medium in the learning process in the world of health

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“…The effects of high-altitude environments range from reduced oxygen to a lower atmospheric pressure. Such effects can in lict hypoxia and have a direct impact on the cardiovascular system [1,2,3,4,5,6]. Electrocardiogram is one of the most common evaluation techniques and offers a wide range of information about a subject's state.…”

Section: Introductionmentioning

confidence: 99%

“…It offers insights about chemical imbalances and electrophysiological conditions. Among the most common conditions are arrhythmias and HRV [3,4,5,7]. HRV deals with the variations of the time intervals be-tween the heart beats.…”

Section: Introductionmentioning

confidence: 99%

A Data Analysis platform for high altitude bio monitoring using DEVOPS principles and time series machine learning

2019

J. adv. tec. eng. res.

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In this paper we propose a software platform for correlating real-time physiological data using microservices and time series analysis with machine learning. We use a devops type architecture with multiple containers governing speci ic aspects of our platform. In order to run the analytics and correlation protocol several microservices had been designed to orchestrate and process multiple data streams in real time. The data ingested by our platform is provided in an intuitive data visualization interface that binds together digital elevation models and geospatial data with the blood oxygen saturation and heart rate of a subject. The platform provides an accurate representation of a subjects physical performance in high altitude/low oxygen environments.

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ECG generation methods for testing and maintenance of cardiac monitors

Cited by 15 publications

References 7 publications

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

ECG Simulator Based on Microcontroller Equipped with Arrhythmia Signal

A Data Analysis platform for high altitude bio monitoring using DEVOPS principles and time series machine learning

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