Multi-Classifier Deep Learning based System for ECG Classification Using Fourier Transform

Eleyan, Alaa; Alboghbaish, Ebrahim

doi:10.1109/biosmart58455.2023.10162124

Cited by 2 publications

(4 citation statements)

References 21 publications

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“…Fast Fourier transform (FFT), a discrete Fourier transform algorithm, solves a wide range of problems, including data filtering, digital signal processing, and partial differential equations. It has been used in many applications such as speech enhancement [31], radar signal processing [32], and ECG classification [12,33]. In this study, fast Fourier transform (FFT), will be used to extract all the frequency components that are contributing to the heartbeat signal including linear and nonlinear components.…”

Section: Feature Extractionmentioning

confidence: 99%

“…This research focuses on developing an automated deep learning model that can classify various classes using a convolutional neural network (CNN) and long short-term memory (LSTM). It has been proven that CNNs can be used for complex applications such as ECG classification [12,33,34]. On the other hand, LSTM is an advanced model that was developed from the recurrent neural networks (RNN) by Hochreiter and Schmidhuber [35].…”

Section: The Proposed Approachmentioning

confidence: 99%

“…Furthermore, the use of AI has developed content-creation software such as Photoshop or Midjourney, in which AI can now generate images or art just from a description of the scene. Moreover, lately many big companies and researchers have been focusing on using AI in biomedical fields such as COVID-19 detection [9,10], cancer detection [11], and arrhythmia detection [12]. Arrhythmia is a kind of heart disease which causes an irregular rhythm within the heart.…”

Section: Introductionmentioning

confidence: 99%

“…They proposed introducing new data augmentation for the ECG signal using simple identical-length segments and re-arrangement to obtain welldistinguished synthetic signals. In other studies, a combination network of convolutional neural network (CNN) and long short-term memory (LSTM) were used in which the major feature selection and classification steps were merged in the deep network [12,24].…”

Section: Introductionmentioning

confidence: 99%

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Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Eleyan,

Alboghbaish

2024

Computers

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Cardiovascular diseases (CVDs) like arrhythmia and heart failure remain the world’s leading cause of death. These conditions can be triggered by high blood pressure, diabetes, and simply the passage of time. The early detection of these heart issues, despite substantial advancements in artificial intelligence (AI) and technology, is still a significant challenge. This research addresses this hurdle by developing a deep-learning-based system that is capable of predicting arrhythmias and heart failure from abnormalities in electrocardiogram (ECG) signals. The system leverages a model that combines long short-term memory (LSTM) networks with convolutional neural networks (CNNs). Extensive experiments were conducted using ECG data from both the MIT-BIH and BIDMC databases under two scenarios. The first scenario employed data from five distinct ECG classes, while the second focused on classifying data from three classes. The results from both scenarios demonstrated that the proposed deep-learning-based classification approach outperformed existing methods.

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Section: Feature Extractionmentioning

confidence: 99%

Section: The Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Eleyan,

Alboghbaish

2024

Computers

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RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction

Eleyan,

AlBoghbaish,

AlShatti

et al. 2024

ASI

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Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future.

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Multi-Classifier Deep Learning based System for ECG Classification Using Fourier Transform

Cited by 2 publications

References 21 publications

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction

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