A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

• Published in: Neural computing & applications Vol. 33; no. 19; pp. 13123 - 13143
• Main Authors: Sharma, Pooja, Dinkar, Shail Kumar, Gupta, D. V.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-10-2021; Springer Nature B.V
• Subjects: Abnormalities; Accuracy; Algorithms; Arrhythmia; Artificial Intelligence; Back propagation networks; Cardiac arrhythmia; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Discrete Wavelet Transform; Electrocardiography; Heart rate; Image Processing and Computer Vision; Machine learning; Neural networks; Optimization; Original Article; Probability and Statistics in Computer Science; Search algorithms; Signal classification; Support vector machines; Support vector machine (SVM); Feed-forward back-propagation neural network (FFBPNN); Cuckoo search algorithm; Arrhythmia; Electrocardiogram (ECG)
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06005-7

This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat abnormalities. The physiological detection using electrocardiogram (ECG) signals has been the most popular means and widely accepted automated detection system to monitor heart health. Additionally, arrhythmia beat classification plays a prominent role in electrocardiogram (ECG) analysis dedicated elucidate cardiac health status while analyzing heart rhythm. The authors aim to classify ECG samples into major arrhythmia classes precisely by removing the inherent noise of ECG signals in preprocessing phase using discrete wavelet transformation (DWT). The QRS complex plays a crucial role in ECG signal identification. Therefore, the position and amplitude of R-peaks are determined to detect the QRS complex. The feature vectors of the QRS complex are further optimized with cuckoo search (CS) optimization algorithm in addition to denoising signals using DWT to select the most relevant set of features. The Support vector machine (SVM)-trained support vector contains the best training information used to train feed-forward back-propagation neural network (FFBPNN) to propose the variant DWT + CS + SVM-FFBPNN to classify signals among five classes. MIT-BIH arrhythmia database is utilized for different types of heartbeats. The classification analysis based on a variant with optimized feature vector using cuckoo search algorithm and SVM-FFBPNN determines heart rate with an accuracy of 98.319%. In contrast, the variant FFBPNN without optimization obtains 97.95% accuracy. The improved performance of the novel combination of classifiers resulted in overall classification accuracy of 98.53% with precision and recall of 98.247% and 95.68%, respectively. The simulation analysis comprising 3600 samples and 1160 heartbeats also outperformed the existing arrhythmia classifications performed based on neural networks. This illustrates the success of the proposed ECG classification model in accurately categorizing ECG signals for arrhythmia classification.