Integrating Morphological Characteristics with Empirical Mode Decomposition for Robust ECG Signal Classification

Abstract

Hyperkalemia, a critical concern, is the primary cause of sudden cardiac deaths in patients with chronic kidney disease (CKD). Traditionally, blood tests serve as the gold standard for hyperkalemia detection. Electrocardiogram (ECG) signals offer a non-invasive means to assess cardiac activity and identify hyperkalemia in CKD patients. Hyperkalemia often presents ECG changes such as elevated T-waves, changes in P-wave morphology, prolonged PR intervals, widened QRS complexes, and, in severe instances, the onset of ventricular arrhythmias and sinusoidal waves. This study proposes a method for the classification of ECG signals for hyperkalaemia using a feature set extracted from electrocardiogram (ECG) signals. Our approach integrates morphological attributes, including P-wave amplitude, T-wave amplitude, QRS interval, PR interval, and ST depression, with spectral attributes such as total power, spectral entropy, variance, skewness, and singular values extracted from Intrinsic Mode Functions obtained through empirical mode decomposition., aiming to capture both structural and frequency domain information inherent in ECG signals. Morphological features provide insights into cardiac abnormalities associated with hyperkalemia and spectral features extracted from IMF, offer valuable information regarding the frequency distribution and complexity of ECG signals. The performance of three classifiers—Kernel Naïve Bayes (KNB), AdaBoost Ensemble Classifier, and Artificial Neural Networks (ANN) is assessed using the extracted features. Among these classifiers, AdaBoost Ensemble Classifier demonstrated the most favorable classification results with sensitivity of 97.7, specificity of 98.84 and accuracy of 98.3%. These findings align with existing state-of-the-art approaches for hyperkalemia classification.