Expert system for detection of congestive heart failure using optimal wavelet and heart rate variability signals for wireless cloud-based environment

Congestive heart failure (CHF) is a cardiac disorder caused due to inefficient pumping of the heart, which leads to insufficient blood flow to the various parts of the body. The electrocardiogram (ECG) is widely used for the detection of heart diseases. However, it is prone to noise resulting in the detection of P, Q, R, S, and T waves ambiguous and erroneous. The heart rate variability (HRV) is considered to be a good indicator of various cardiac abnormalities. Hence, HRV is preferred. HRV can depict the magnitude of pumping of the heart in the RR interval signals accurately. This work proposes a method to automatically identify CHF using two-band stopband energy (SBE) optimized orthogonal wavelet filter bank with HRV signals. In the proposed method, we have segmented the HRV data into lengths of 500 and 2000 samples. The HRV signals are decomposed into six sub-bands, and the wavelet coefficients obtained are used for the extraction of fuzzy entropy (FE) and log energy (LE) features. The extracted features are utilized to classify HRV signals into control and CHF-affected patients using support vector machine (SVM), bagged tree, complex tree, k-nearest neighbour (KNN), and linear discriminant classifiers. The SVM performed better than other classifiers yielding the classification accuracy >95.20% and maximum classification accuracy of 99.30% with (2000 samples) using cubic SVM (CSVM). The 10-fold cross-validation method is employed during classification to reduce the over-fitting phenomenon (Sharma, Dhiman, & Acharya, 2021). It appears that the proposed optimal wavelet-based automated system can identify CHF accurately using HRV signals. Hence, the model may be applied in clinical usage during an emergency employing a cloud-based wireless system after testing the developed model with more data.