Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model

• Autorzy: Rahul Jagdeep , Sharma Lakhan Dev

Identyfikatory

DOI

10.1016/j.bbe.2022.02.006

• Języki publikacji: EN

Abstrakty

EN

Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can cause chest pain and trouble breathing, especially while active. However, some patients with heart disease have no symptoms and may benefit from screening. Electrocardiogram (ECG) measures electrical activity of the heart using sensors positioned on the skin over the chest, and it can be used for the timely detection of CVDs. This work presents a technique for classification among lethal CVDs like atrial fibrillation (Afib), ventricular fibrillation (Vfib), ventricular tachycardia (Vtec), and normal (N) beats. A novel combination of Stationary wavelet transforms (SWT) and a two-stage median filter with Savitzky–Golay (SG) filter were utilised for pre-processing of the ECG signal followed by segmentation and z-score normalisation process. Next, 1-D six-layers convolutional neural network (1- D CNN) was used for automated and reliable feature extraction. After that, bidirectional long short-term memory (Bi-LSTM) was used in the back end for classification of arrhythmias. The novelty of the present work is the use of 1-D CNN and Bi-LSTM architecture followed by relevant and effective pre-processing of the ECG signal makes this technique accurate and reliable. An accuracy of 99.41 % was achieved using 10-fold cross validation, which is superior to the existing state-of-art methods. Thus, this method presents a noble, accurate, and reliable method for classification of cardiac arrhythmia beats.

Słowa kluczowe

EN

CVDs; cardiovascular diseases; ECG; electrocardiogram; SWT; stationary wavelet transform; 1-D CNN; Bi-LSTM

PL

CVDs; choroba sercowo-naczyniowa; EKG; elektrokardiogram; SWT; stacjonarna transformacja falkowa; 1-D CNN; Bi-LSTM

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 312--324
• Opis fizyczny: Bibliogr. 60 poz., rys., tab., wykr.

Twórcy

autor

Rahul Jagdeep

• Department of Electronics & Communication Engineering, Rajiv Gandhi University, India

autor

Sharma Lakhan Dev

• School of Electronics Engineering, VIT-AP University, India

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