Implementation of ECG sensor for real time signal processing applications

Gawali, Dhanashri H.; Wadhai, Vijay M.

doi:10.1109/icaecc.2014.7002435

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…An electric activity of a heart muscle is measured by placing an electrode at outer surface of the skin, as the heart performing of its function to pump the blood through the circulation system. Electrode is used for sensing bioelectrical potential caused by muscle cells, as a result of mechanical events inside heart muscles are a certain cycle of electrical activity or ECG signal [5], [6]. Thus, electrodes used in this study and location of its placements, to acquire ECG signal as shown in Fig.…”

Section: A Signal Inputmentioning

confidence: 99%

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Implementation of Wireless Electrocardiogram Monitoring System

Chaichana¹,

Pititeeraphab²,

Sangworasil³

et al. 2016

IJEEE

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We describe the design and development of wireless electrocardiogram monitoring system to meet the biomedical needs. Our system is able to acquire, display and record the patient's real time electrocardiogram (ECG) data. Patient data received from this system can be further used for analysis of abnormal heart rate and new clinical applications. System hardware comprises of an electrode as part of signal input, signal conditioning components for manipulating signal, the 8051 microcontroller unit to perform signal processing and wireless communication module. Software was developed in Visual Basic 6 to record and visualise ECG signal in real time. Therefore, we implemented ECG monitor system, and this can be used to advanced ECG study. 

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Section: A Signal Inputmentioning

confidence: 99%

“…Typically, ECG signal obtained from an electrode was very small amplitude, and ranged around ±2mV, is a very weak signal [5]. Thus, an accurate amplification of ECG signal is a crucial practice, and required to further analysis and displaying a proper ECG waveform.…”

Section: B Signal Conditioningmentioning

confidence: 99%

Implementation of Wireless Electrocardiogram Monitoring System

Chaichana¹,

Pititeeraphab²,

Sangworasil³

et al. 2016

IJEEE

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“…There are many studies using hardware implementation with the aim of helping experts in detecting heart abnormalities quickly. Gawali et al [8] develop a real-time ECG sensor that can acquire and store patient ECG data in real-time. Such patient data can be used for any real-time processing application.…”

Section: Introductionmentioning

confidence: 99%

An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction

et al. 2022

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This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.

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