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

Abstract

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.

Numerical investigation of hemodynamic pattern in carotid artery dynamic aneurysm on bifurcation region for early clinical decision making

“…One of the cases has been simulated assuming a compliant wall boundary condition. From existing literature (Canchi et al, 2018; Sharma et al, 2021), it has been observed that the aneurysm growth is due to wall thinning caused by the removal of SMC in the artery wall and it has been analysed using analytical calculations in both rest and exercise conditions. Since it is not possible to numerically predict the wall thinning and simulate the aneurysm growth, aneurysm has been built artificially near the bifurcation region, the variation in the flow pattern and the WSS in the aneurysm region was monitored for both rest and exercise condition.…”

“…Very limited literature is available to understand the effect of WSS in CA aneurysm in bifurcation region (rigid and compliant vessel) including dynamic growth of aneurysm in rest and exercise conditions and if such analysis is carried out it would be a good addition in systematic approach for pre-planning of necessary clinical interventions. There are some advanced expert systems exist in literature for automatic identification of cardiac related abnormalities (Sharma et al, 2021). In the present work, a CA model from clinical data with fusiform aneurysm in rigid and complaint condition has been developed and the effects of WSS in hemodynamic pattern near the bifurcation region in rest and exercise conditions using CFD is presented for early decision making.…”

Human activity recognition based on multi‐instance learning

“…Machine learning algorithms have been widely used in different fields such as smart city applications (Tiwari et al, 2021), score prediction (Birant, 2021), medical diagnosis (Sharma et al, 2021), and fraud detection (Forough & Momtazi, 2022). Besides these topics, human activity recognition (HAR) is an emerging field, in which machine learning (ML) techniques are used to generate a classifier that learns activities from a set of observations of individuals' actions.…”

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

“…This process consumes time and requires sufficient expertise. To negate these, HPT is often detected using computational intelligence-based diagnostic techniques [ 3 , 6 , 7 ].…”