1D CNN model for ECG diagnosis based on several classifiers
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Дата
2022
Науковий керівник
Назва журналу
Номер ISSN
Назва тому
Видавець
КПІ ім. Ігоря Сікорського
Анотація
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.
Опис
Ключові слова
Electrocardiogram (ECG), Continuous wavelet transform (CWT), 1D convolutional neural network (CNN) model, електрокардіограма (ECG), безперервне вейвлет-перетворення (CWT), одновимірна модель згорткової нейронної мережі (CNN)
Бібліографічний опис
1D CNN model for ECG diagnosis based on several classifiers / Mahmoud M. Bassiouni, Islam Hegazy, Nouhad Rizk, El-Sayed A. El-Dahshan, Abdelbadeeh M. Salem // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2022. – № 4. – С. 7-20. – Бібліогр.: 27 назв.