Fully Elman Neural Network: A Novel Deep Recurrent Neural Network
Optimized by an Improved Harris Hawks Algorithm for Classification of
Pulmonary Arterial Wedge Pressure
Abstract
Heart failure (HF) is one of the most prevalent life-threatening
cardiovascular diseases in which 6.5 million people are suffering in the
USA and more than 23 million worldwide. Mechanical circulatory support
of HF patients can be achieved by implanting a left ventricular assist
device (LVAD) into HF patients as a bridge to transplant, recovery or
destination therapy and can be controlled by measurement of normal and
abnormal pulmonary arterial wedge pressure (PAWP). While there are no
commercial long-term implantable pressure sensors to measure PAWP,
real-time non-invasive estimation of abnormal and normal PAWP becomes
vital. In this work, first an improved Harris Hawks optimizer algorithm
called HHO+ is presented and tested on 24 unimodal and multimodal
benchmark functions. Second, a novel fully Elman neural network (FENN)
is proposed to improve the classification performance. Finally, four
novel 18-layer deep learning methods of convolutional neural networks
(CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural
networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and
CNN with fully Elman neural networks optimized by HHO+ algorithm
(CNN-FENN-HHO+) for classification of abnormal and normal PAWP using
estimated HVAD pump flow were developed and compared. The estimated pump
flow was derived by a non-invasive method embedded into the commercial
HVAD controller. The proposed methods are evaluated on an imbalanced
clinical dataset using 5-fold cross-validation. The proposed
CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and
CNN-FENN methods and improved the classification performance metrics
across 5-fold cross-validation with an average sensitivity of 79%,
accuracy of 78% and specificity of 76%. The proposed methods can
reduce the likelihood of hazardous events like pulmonary congestion and
ventricular suction for HF patients and notify identified abnormal cases
to the hospital, clinician and cardiologist for emergency action, which
can diminish the mortality rate of patients with HF.