Why the study?
Deep learning techniques seem promising for healthcare prediction problems requiring ECG data, motivating their application to classify arrhythmias.
Does a deep learning model based on convolutional neural networks accurately classify normal and abnormal ECG heartbeats in the MIT-BIH arrhythmia database?
Does a deep learning model based on convolutional neural networks accurately classify normal and abnormal ECG heartbeats in the MIT-BIH arrhythmia database?
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A novel deep learning model using convolutional neural networks and spectrograms of ECG signals achieved 99.22% accuracy in detecting normal and abnormal heartbeats, offering a promising tool for automated arrhythmia classification.
One of the most often used diagnostic tools in medicine and healthcare is the electrocardiogram (ECG). When it comes to healthcare prediction problems requiring ECG data, deep learning techniques seem promising. This paper aims to apply deep learning techniques to classify MIT-BIH arrhythmias on publicly available datasets. A new electrocardiogram classification for employing a spectrogram of signals algorithm is proposed. The proposed model depends on convolutional neural networks to automatically learn the characteristics of features and has used convolutional neural networks to detect normal and abnormal ECG heartbeats, with an average detection accuracy of 99.22%.
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Amira Ahmed (2023) studied Arrhythmia (n=47). Convolutional Neural Network (CNN) was evaluated on Average detection accuracy of normal and abnormal ECG heartbeats.
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