基于混合Resnet和双向lstm的心血管疾病检测深度学习模型

Kalaiselvi Balaraman, Angelin Claret S.P.
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引用次数: 1

摘要

高血压是血压(BP)的主要根本原因,而血压(BP)又会导致各种心血管疾病(cvd)。因此,需要定期监测血压以预防心血管疾病,因为它可以通过持续观察来诊断和控制。光容积脉搏波(PPG)是一种重要的低成本技术,为cvd的早期检测提供了方便和有效的方法。利用PPG技术可以测定不同的心血管参数,如血氧饱和度、心率、血压等。当将这些心血管参数作为输入输入到深度学习模型时,确定以最大的准确性诊断cvd达到预期水平。本文提出了基于混合ResNet和双向lstm的深度学习模型(HRBLDLM),用于从PPG信号中诊断cvd,在连续监测过程中为医生提供支持。该深度学习模型主要集中于利用PPG信号对1期高血压、2期高血压、高血压前期和正常cvd进行诊断,准确率最高。研究人员使用基于物联网的可穿戴患者监测(WPM)设备记录了从PPG- bp数据集中确定的用于调查的PPG信号,这些信号发生在身体活动期间,包括高强度、中强度和低强度的运动,包括驾驶、坐着和行走。使用PPG-BP数据集对所提出的深度学习模型进行的实验证实,与用于检测cvd的基于ppg的基线深度学习模型相比,该模型的分类准确率达到99.62%。
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Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals
Hypertension is the major root cause of blood pressure (BP) which in turn causes different cardiovascular diseases (CVDs). Hence BP need to be regularly monitored for preventing CVDs since it can be diagnosed and controlled through constant observation. Photoplethysmography (PPG) is identified as an important low-cost technology for facilitating a convenient and effective process in the early detection of CVDs. Different cardiovascular parameters such as blood oxygen saturation, heart rate, blood pressure, etc can be determined using the PPG technology. These cardiovascular parameters when given as input to the deep learning model is determined to diagnosis CVDs with maximized accuracy to an expected level. In this paper, Hybrid ResNet and Bidirectional LSTM-based Deep Learning Model (HRBLDLM) is proposed for diagnosing CVDs from PPG signals with due help in supporting the physicians during the process of continuous monitoring. This deep learning model mainly concentrated on the diagnosis of stage 1 hypertension, stage 2 hypertension, prehypertension, and normal CVDs with maximized accuracy using PPG signals. The PPG signals determined from PPG-BP dataset for investigation were recorded using IoT-based wearable patient monitoring (WPM) devices during the physical activity that includes high intensity, medium and low intensity movements involved driving, sitting and walking. The experiments conducted for this proposed deep learning model using PPG-BP dataset confirmed a better classification accuracy of 99.62% on par with the baseline PPG-based deep learning models contributed for detecting CVDs.
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