Wavelet Scattering Transform for ECG Beat Classification
المؤلفون المشاركون
Liu, Zhishuai
Yao, Guihua
Zhang, Qing
Zhang, Junpu
Zeng, Xueying
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-09
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia.
However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity.
The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators.
We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats.
In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat.
Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows.
These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification.
The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation.
Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Zhishuai& Yao, Guihua& Zhang, Qing& Zhang, Junpu& Zeng, Xueying. 2020. Wavelet Scattering Transform for ECG Beat Classification. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139390
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Zhishuai…[et al.]. Wavelet Scattering Transform for ECG Beat Classification. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139390
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Zhishuai& Yao, Guihua& Zhang, Qing& Zhang, Junpu& Zeng, Xueying. Wavelet Scattering Transform for ECG Beat Classification. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139390
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139390
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر