The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform
Raja Majid Mehmood - One of the best experts on this subject based on the ideXlab platform.
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classification of arrhythmia by using deep learning with 2 d ecg spectral image representation
Remote Sensing, 2020Co-Authors: Amin Ullah, Syed Muhammad Anwar, Muhammad Bilal, Raja Majid MehmoodAbstract:The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal Beat, premature Ventricular contraction Beat, paced Beat, right bundle branch block Beat, left bundle branch block Beat, atrial premature contraction Beat, Ventricular flutter wave Beat, and Ventricular Escape Beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
Amin Ullah - One of the best experts on this subject based on the ideXlab platform.
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classification of arrhythmia by using deep learning with 2 d ecg spectral image representation
Remote Sensing, 2020Co-Authors: Amin Ullah, Syed Muhammad Anwar, Muhammad Bilal, Raja Majid MehmoodAbstract:The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal Beat, premature Ventricular contraction Beat, paced Beat, right bundle branch block Beat, left bundle branch block Beat, atrial premature contraction Beat, Ventricular flutter wave Beat, and Ventricular Escape Beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
Syed Muhammad Anwar - One of the best experts on this subject based on the ideXlab platform.
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classification of arrhythmia by using deep learning with 2 d ecg spectral image representation
Remote Sensing, 2020Co-Authors: Amin Ullah, Syed Muhammad Anwar, Muhammad Bilal, Raja Majid MehmoodAbstract:The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal Beat, premature Ventricular contraction Beat, paced Beat, right bundle branch block Beat, left bundle branch block Beat, atrial premature contraction Beat, Ventricular flutter wave Beat, and Ventricular Escape Beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
Muhammad Bilal - One of the best experts on this subject based on the ideXlab platform.
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classification of arrhythmia by using deep learning with 2 d ecg spectral image representation
Remote Sensing, 2020Co-Authors: Amin Ullah, Syed Muhammad Anwar, Muhammad Bilal, Raja Majid MehmoodAbstract:The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal Beat, premature Ventricular contraction Beat, paced Beat, right bundle branch block Beat, left bundle branch block Beat, atrial premature contraction Beat, Ventricular flutter wave Beat, and Ventricular Escape Beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.
Mehmood, Raja M - One of the best experts on this subject based on the ideXlab platform.
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Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
'MDPI AG', 2020Co-Authors: Ullah Amin, Anwar, Syed M., Bilal Muhammad, Mehmood, Raja MAbstract:The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal Beat, premature Ventricular contraction Beat, paced Beat, right bundle branch block Beat, left bundle branch block Beat, atrial premature contraction Beat, Ventricular flutter wave Beat, and Ventricular Escape Beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa
