Development of Electrocardiograph Monitoring System

This study presents an electrocardiogram (ECG) monitoring and processing system which can observe subjects in real time and display the resultant ECG signals on a computer for observation. The primary application is for the remote observation of cardiac patients. This paper aims to determine its reliability by analysing its portability and wireless connectivity. The system is comprised of three principal units, namely the data acquisition circuit, where cardiac electrical signals are detected using three surface electrodes placed at three different positions on the chest wall to follow the Einthoven Triangle. The signals measured are amplified and filtered by components in a circuit and are then carried to a data processing unit where a ATmega328P microcontroller with a ZigBee interface module are used to transfer the biosignal wirelessly to the Graphical User Interface (GUI) unit which has the capacity to observe ECG biosignals on a computer. The results demonstrated that the design successfully produced a distortion-free signal, namely the hardware and software elements operated and intercommunicated correctly. In both LabVIEW and MATLAB configurations, the GUI characteristics were examined and found to yield unproblematic, user-friendly displays in real-time. Thus, this research provides a novel ECG system design to effectively analyse cardiac patients, however, it would be useful to develop a tool that can differentiate the various forms of cardiac arrhythmia.

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“…20b [17], and MATLAB Fig. 20c [18]. As result, our ECG signal with both platform LabVIEW and MATLAB showed high Fig.…”

Section: Gui Test and Resultsmentioning

confidence: 88%

“…Fig. 20 The validation results a the data generated by the existing ECG machine, b the results of real time ECG monitoring system [17], c the results of developed ECG monitoring system [18]…”

Section: Further Researchmentioning

confidence: 99%

A portable electrocardiogram for real-time monitoring of cardiac signals

2020

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“…A heartbeat is classified as regular, or irregular based on its beat structure. The structure is composed of a P-wave, QRS complex, T and U waves [14]. The total length of a beat is 0.6 to 0.8 seconds on the time axis.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Heartbeat Classification using ECG Signals and Convolutional Neural Networks

Qureshi

Camara

Poorter

et al. 2022

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)

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Given a large enough time series signal from an ECG signal, it is possible to identify and classify heartbeats not only into normal and abnormal classes but into multiple classes including but not limited to Normal beat, Paced beat, Atrial Premature beat and Ventricular flutter as originally suggested by benchmark electrocardiogram (ECG) datasets like the MIT-BIH Arrhythmia Dataset. There are multiple approaches that target ECG classifications using Machine and Deep Learning like One Class SVM, ELM, Anogan etc. These approaches require either very high computational resources, fail to classify classes apart from normal/abnormal classes or fail to classify all classes with an equivalent or near-equivalent accuracy. With these limitations in mind, this paper proposes a deep learning approach using Convolutional Neural Networks (CNNs) to classify multiple classes of heartbeats in an efficient, effective, and generalized manner. By using the MIT-BIH Arrhythmia dataset to filter and segment individual correctly structured heartbeats, we have designed a network which can be trained on different classes of heartbeats and present robust, accurate and efficient results. The class imbalance prevalent in the MIT-BIH dataset has been dealt with using Synthetic Minority Over-sampling Technique (SMOTE). The robustness of the model is increased by adding techniques of loss minimization such as dropout and early stopping. The approach gives an accuracy of approximately 96% and an extremely short time span for class prediction(classification), i.e., less than 1 second. The results are also illustrated over multiple (10) classes to exemplify the generality of the model. We have illustrated these results over multiple (10) classes to exemplify generality of the model.

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“…The long-term and continuous monitoring of human bio-signals such as electrocardiography (ECG) [ 1 , 2 , 3 ], electromyography (EMG) [ 4 , 5 , 6 ], electroencephalography (EEG) [ 7 , 8 , 9 ], and electrooculography (EOG) [ 10 , 11 ] enables early medical diagnosis and recovery from disease. Thus, technologies to accurately monitor human bio-signals are required to reduce the severity of disease [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Biodegradable Polymer Composites for Electrophysiological Signal Sensing

2022

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Electrophysiological signals are collected to characterize human health and applied in various fields, such as medicine, engineering, and pharmaceuticals. Studies of electrophysiological signals have focused on accurate signal acquisition, real-time monitoring, and signal interpretation. Furthermore, the development of electronic devices consisting of biodegradable and biocompatible materials has been attracting attention over the last decade. In this regard, this review presents a timely overview of electrophysiological signals collected with biodegradable polymer electrodes. Candidate polymers that can constitute biodegradable polymer electrodes are systemically classified by their essential properties for collecting electrophysiological signals. Moreover, electrophysiological signals, such as electrocardiograms, electromyograms, and electroencephalograms subdivided with human organs, are discussed. In addition, the evaluation of the biodegradability of various electrodes with an electrophysiology signal collection purpose is comprehensively revisited.

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Development of Electrocardiograph Monitoring System

Cited by 5 publications

References 4 publications

A portable electrocardiogram for real-time monitoring of cardiac signals

A portable electrocardiogram for real-time monitoring of cardiac signals

Multiclass Heartbeat Classification using ECG Signals and Convolutional Neural Networks

Biodegradable Polymer Composites for Electrophysiological Signal Sensing

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