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4 results on '"1d convolutional neural network (cnn) model"'

1. 1D CNN model for ECG diagnosis based on several classifiers

Author

Mahmoud Bassiouni, Islam Hegazy, Nouhad Rizk, El-Sayed El-Dahshan, and Abdelbadeeh Salem

Subjects
electrocardiogram (ecg), continuous wavelet transform (cwt), 1d convolutional neural network (cnn) model, Electronic computers. Computer science, QA75.5-76.95
Abstract

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. more...

Published
2022
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2. 1D CNN MODEL FOR ECG DIAGNOSIS BASED ON SEVERAL CLASSIFIERS.

Author

BASSIOUNI, MAHMOUD M., HEGAZY, ISLAM, RIZK, NOUHAD, EL-DAHSHAN, EL-SAYED A., and SALEM, ABDELBADEEH M.

Subjects
ARRHYTHMIA, HEART disease diagnosis, EARLY diagnosis, CONVOLUTIONAL neural networks, ELECTROCARDIOGRAPHY, MYOCARDIUM
Abstract

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. [ABSTRACT FROM AUTHOR] more...

Published
2022
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3. 1D модель CNN для діагностики ЕКГ на кількох класифікаторах

Subjects
електрокардіограма (ECG), одновимірна модель згорткової нейронної мережі (CNN), 1D convolutional neural network (CNN) model, Electrocardiogram (ECG), Continuous wavelet transform (CWT), безперервне вейвлет-перетворення (CWT)
Abstract

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases., Однією з основних причин смерті людини є захворювання серця. Виявлення серцевих захворювань на ранній стадії може запобігти серцевій недостатності або будь-якому пошкодженню серцевого м’яза. Одним з основних сигналів, які можуть бути корисними в діагностиці захворювань серця, є електрокардіограма (ЕКГ). Розглянуто діагностику чотирьох типів записів ЕКГ, таких як інфаркт міокарда (MYC), норма (N), відхилення сегмента ST (ST) і надшлуночкова аритмія (SV). Методологія збирає дані з шести основних наборів даних, а потім записи ЕКГ фільтруються за допомогою ланцюжка попереднього оброблення. Після цього запропонована модель 1D CNN використовується для вилучення ознак із записів ЕКГ. Потім застосовуються два різні класифікатори, щоб перевірити ефективність виділених ознак і отримати надійну точність діагностики. Два класифікатори – це softmax і класифікатор випадкового лісу (RF). Застосовується експеримент для діагностики чотирьох типів записів ЕКГ. Зрештою найвищої продуктивності досягнуто за допомогою радіочастотного класифікатора з точністю 98,3%. Порівняння з іншими суміжними роботами показало, що запропоновану методику можна застосовувати для раннього виявлення захворювань серця. more...

Published
2022

4. 1D CNN model for ECG diagnosis based on several classifiers

Abstract

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. more...

Published
2022