The Real-time Electrocardiogram Signal Monitoring System in Wireless Sensor Network

Muankid, Anchana; Ketcham, Mahasak

doi:10.3991/ijoe.v15i02.9422

Cited by 23 publications

(3 citation statements)

References 8 publications

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“…We use the ECG kit circuit for feedback of a real-time signal. [3][6] The medical device industry understands embedded engineering. Embedded engineering has been increasingly complex, making it more difficult for non-technical experts to build fullfeatured medical device models.…”

Section: Electronic Sensor Kitmentioning

confidence: 99%

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Naijit

2020

Int. J. Onl. Eng.

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<p class="p1">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.</p>

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Section: Electronic Sensor Kitmentioning

confidence: 99%

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Naijit

2020

Int. J. Onl. Eng.

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“…This number surpassed the death caused by stroke and respiratory disease globally. Heart disease can be detected from an electrocardiogram (ECG) signal [4]. The signal pattern can then be analyzed to detect heart rhythm abnormalities or irregular heartbeat, also known as arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

Image Based ECG Signal Classification Using Convolutional Neural Network

et al. 2022

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Electrocardiogram (ECG) analysis is one of the gold standards in diagnosing heart abnormalities. Commonly, clinicians analyze the ECG signal visually by observing the shape, rhythm, and voltage of the signal. Some of them are supported by the application of automatic diagnosis of the ECG device itself. Currently, digital signal processing combined with traditional or advanced machine learning plays an important role in supporting medical diagnosis including ECG diagnosis. However, it is often constrained by the lack of raw data support from most commercial ECG devices. Classification method by processing ECG image can be one way to tackle this problem. Therefore, in this preliminary study, an image-based ECG classification method using a deep learning approach is proposed. The ECG signals analyzed in this study include normal sinus rhythm (NSR), premature ventricular contraction (PVC), and Bigeminy. Convolutional neural network (CNN) with VGG16 architecture has been employed for feature extraction and classification. The simulation results show up to 95% accuracy in detecting ECG abnormalities. The results of this study can be an alternative in detecting ECG abnormalities and can be considered as a supporting diagnosis by the clinician.

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“…It is an IEEE 802.15.4 based specification used to create wireless networks that require a low data transfer rate, increased energy efficiency and secure networking [32]. The performance of both the ZigBee and Bluetooth is somewhat similar; however, ZigBee is more commonly used in relaying data for environmental monitoring [33], home automation, industrial automation, security systems and smart lighting applications.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Low Cost Wireless Ambulatory ECG Monitoring System for Deployment in Rural Communities

2019

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Due to the rising number of heart patients and the apparent need for more robust electrocardiogram (ECG) monitoring of these patients, hospitals are increasingly investing in typical cloud technology or centralized hospital server based remote ECG monitoring systems. However, the deployment these systems in rural communities is limited due to the high cost factor. To counter this challenge, in this paper, we focus on the design and implementation of a low cost real time wireless ambulatory ECG monitoring system. The detected ECG signals are first filtered and amplified and then digitally converted by a microcontroller. The digitized ECG signals are then sent over a ZigBee wireless link to a gateway personal computer (PC) at patient’s premises. The received ECG data from the ZigBee connection is displayed in real time via the National Instruments (NI) Laboratory Virtual Instrument Engineering Workbench (LabVIEW) user interface on the PC for instant personalized evaluation of the ECG data. The ECG data can be saved on the PC and sent via email to a remote cardiologist or a clinician. Additionally, the gateway PC at patient’s end acts as web server for sharing patient’s data over the Internet. The remote off-site physician (medical staff in a hospital) can use a web browser on a PC, laptop or a mobile phone with Internet connection to access patient’s real time ECG trace for monitoring, expert review and diagnosis. It is shown that the system prototype allows users to acquire reliable ECG signals effectively and simply. The proposed ambulatory ECG system offers an alternative low cost deployment strategy and is especially suited for remote cardiac monitoring of patients in rural communities.

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The Real-time Electrocardiogram Signal Monitoring System in Wireless Sensor Network

Cited by 23 publications

References 8 publications

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Image Based ECG Signal Classification Using Convolutional Neural Network

Design and Implementation of a Low Cost Wireless Ambulatory ECG Monitoring System for Deployment in Rural Communities

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