PMID- 34813462
OWN - NLM
STAT- MEDLINE
DCOM- 20220425
LR  - 20220505
IS  - 1558-2531 (Electronic)
IS  - 0018-9294 (Linking)
VI  - 69
IP  - 5
DP  - 2022 May
TI  - Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an 
      Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge 
      Pressure.
PG  - 1733-1744
LID - 10.1109/TBME.2021.3129459 [doi]
AB  - 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.
FAU - Fetanat, Masoud
AU  - Fetanat M
FAU - Stevens, Michael
AU  - Stevens M
FAU - Jain, Pankaj
AU  - Jain P
FAU - Hayward, Christopher
AU  - Hayward C
FAU - Meijering, Erik
AU  - Meijering E
FAU - Lovell, Nigel H
AU  - Lovell NH
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220421
PL  - United States
TA  - IEEE Trans Biomed Eng
JT  - IEEE transactions on bio-medical engineering
JID - 0012737
SB  - IM
MH  - Algorithms
MH  - *Heart-Assist Devices
MH  - Humans
MH  - *Hypertension, Pulmonary
MH  - Neural Networks, Computer
MH  - Pulmonary Wedge Pressure
EDAT- 2021/11/24 06:00
MHDA- 2022/04/26 06:00
CRDT- 2021/11/23 17:15
PHST- 2021/11/24 06:00 [pubmed]
PHST- 2022/04/26 06:00 [medline]
PHST- 2021/11/23 17:15 [entrez]
AID - 10.1109/TBME.2021.3129459 [doi]
PST - ppublish
SO  - IEEE Trans Biomed Eng. 2022 May;69(5):1733-1744. doi: 10.1109/TBME.2021.3129459. 
      Epub 2022 Apr 21.