Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing

Bogdanov, Momcilo; Baigildin, Salim; Fabarisova, Aigul; Ushenin, Konstantin; Solovyova, Olga

doi:10.1109/usbereit48449.2020.9117753

Cited by 2 publications

(9 citation statements)

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“…The ECG signal is represented as a waveform with peaks and valleys that form the PQRST pattern [6,[8][9][10]. The deflections of the sinus rhythm represent the events of the atria and the ventricles of the heart in the normal rhythm.…”

Section: Introductionmentioning

confidence: 99%

“…The deflections of the sinus rhythm represent the events of the atria and the ventricles of the heart in the normal rhythm. The morphological characteristics of the heartbeat vary from person to person and for the same person under different circumstances [8][9][10]. The amplitudes of the P wave, QRS complex, S wave, T wave, and the intervals namely RR, PR, QT, and QRS complex are used to measure the health condition of the heart [6].…”

Section: Introductionmentioning

confidence: 99%

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An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

Obeidat

Alqudah

2023

Applied Sciences

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In recent years, there has been a growing demand for affordable and user-friendly medical diagnostic devices due to the rise in global diseases. This study focuses on the development of an embedded system based on Raspberry Pi that enables faster and more efficient monitoring of electrocardiogram (ECG). The incorporation of Raspberry Pi allows for both wireless and wired interfaces, facilitating the creation of an ECG diagnostic embedded system capable of real-time detection and immediate response to any abnormalities in heart functionality. The system presented in this research encompasses a comprehensive electronic circuit comprising analog and digital components to measure and display the ECG signal. Within the analog section, the circuit performs essential signal conditioning tasks, such as signal amplification and noise filtering, ensuring a clean signal within the desired frequency range. The entire system is powered using a power bank. The digital segment incorporates an analog-to-digital converter necessary for converting the received analog signal into a digital format compatible with Raspberry Pi. A graphical liquid-crystal display is utilized to display the measured signal. The device successfully measures ECG signals at various heart rates, capturing all crucial peaks that can be used as indicators of an individual’s health condition. By comparing the signals obtained from healthy individuals with those exhibiting heart arrhythmias, valuable insights can be gained regarding their health status. The proposed system aims to be portable, cost-effective, and user-friendly in different environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

Obeidat

Alqudah

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Electrocardiogram (ECG) is a non-stationary physiological signal that represents the heart's electrical activity. ECG is normally represented as a waveform in PQRST pattern that has peaks and valleys [3,8,9]. In a normal heart rhythm, the events of the atria and the ventricles of the heart are represented in the deflections of the sinus rhythm.…”

Section: Introductionmentioning

confidence: 99%

“…The condition of the heart is measured by the amplitudes of the P wave, QRS complex, S wave, T wave, and the intervals namely RR, PR, QT, and QRS complex. Predicting the changes of these measures from the normal values is an indicator of an irregular cardiac cycle patterns called Arrhythmia [7][8][9][10][11][12]. The irregular pattern in the rhythm of ECG is usually observed by physicians to identify the type of cardiac arrhythmia.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing

Cited by 2 publications

References 20 publications

An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

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