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.

翻译：心脏衰竭(高频)是美国和全世界2 300万以上患者最普遍的危及生命的心血管疾病之一,其中美国有650万人罹患这种疾病。 通过将左心室辅助装置(LVAD)植入高频患者体内,作为移植、恢复或目的地治疗的桥梁,并且通过测量正常和异常肺动脉动网压(PAWP)加以控制,可以实现高频患者的机械循环支持。虽然没有商业长期可移植压力传感器来测量PAWP,但对异常和正常的PAWP进行实时非侵入性估算。在这项工作中,首先可以在24个单式和多式联运基准功能上展示和测试名为HHHO+的哈里哈里斯·霍克斯优化算(Harris Haws)算(HHO+Mal-Ralder)算法。最后,4个新的18层深层革命神经神经网络(CN-NCRV-MLP)的升级跨感官、与El-NFO-NFO-M-MOL-S-S-SOL-S-SOL-SOL-SOL-SOL-SOL-SOL-SOL-SOL-S-SOL-SOL-SOL-I-I-IOL-O-ILVLVOL-S-S-S-S-ID-S-ILVLVD-S-S-S-S-ID-S-S-S-S-S-S-S-ILVD-ILVOL-ID-ID-NLVLVLVLVOL-S-S-S-ID-S-S-S-S-S-ID-ID-ID-S-S-S-S-S-S-I-I-I-S-I-I-I-I-S-S-S-S-S-S-IOL-IOL-NLVOL-ID-ID-ID-ID-S-ID-ID-ID-I-I-I-S-I-I-S-ID-S-ID-ID-ID-I-I-I-I-I-IOL-I-I-I-