Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach.,International Journal for Numerical Methods in Biomedical Engineering

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Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach.
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2020-02-04 , DOI: 10.1002/cnm.3303
Janne M J Huttunen ₁ , Leo Kärkkäinen _{1,

2} , Mikko Honkala ₁ , Harri Lindholm ₁

Affiliation

Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with a virtual database approach where training data is generated using computer simulations of related phenomena. Specifically, we concentrate on the following problem: can cardiovascular indices such as aortic elasticity, diastolic and systolic blood pressures, and blood flow from heart be predicted continuously using wearable photoplethysmographic sensors? We simulate the blood flow using a haemodynamic model consisting of the entire human circulation. Repeated evaluation of the simulator allows us to create a database of “virtual subjects” with size that is only limited by available computational resources. Using this database, we train neural networks to predict the cardiac indices from photoplethysmographic signal waveform. We consider two approaches: neural networks based on predefined input features and deep convolutional neural networks taking waveform directly as the input. The performance of the methods is demonstrated using numerical examples, thus carrying out a preliminary assessment of the approaches. The results show improvements in accuracy compared with the previous methods. The improvements are especially significant with indices related to aortic elasticity and maximum blood flow. The proposed approach would provide new means to measure cardiovascular health continuously, for example, with a simple wrist device.

中文翻译：

深度学习可通过光电容积描记波形预测心脏指数：一种虚拟数据库方法。

深度学习方法与大型数据集相结合，最近显示出在解决多项医学任务方面的重大进步。但是，收集和注释大型数据集可能是非常繁琐且昂贵的任务。我们使用虚拟数据库方法解决了这些问题，其中使用相关现象的计算机模拟来生成训练数据。具体来说，我们集中于以下问题：使用可穿戴式光电容积描记器可以连续预测心血管指数，例如主动脉弹性，舒张压和收缩压以及来自心脏的血流量吗？我们使用由整个人体循环组成的血液动力学模型模拟血流。对模拟器的反复评估使我们能够创建一个“虚拟对象”数据库，其大小仅受可用计算资源的限制。使用该数据库，我们训练神经网络以根据光电容积描记器信号波形预测心脏指数。我们考虑两种方法：基于预定义输入特征的神经网络和直接将波形作为输入的深度卷积神经网络。通过数值示例证明了该方法的性能，从而对方法进行了初步评估。结果表明与以前的方法相比，准确性有所提高。这些改进在与主动脉弹性和最大血流量有关的指标方面尤为重要。拟议的方法将提供新的方法来连续测量心血管健康，

更新日期：2020-02-04

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