  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. 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.
