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Estimation of the Heart Rate Variability Features via Recurrent Neural Networks

机译：经常性神经网络估计心率变异性特征

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Heart rate variability(HRV)analysis has increasingly become a promising marker for the assessment of the autonomic nervous system.The easy derivation of the HRV has determined its popularity,being successfully used in many research and clinical studies.However,the conventional HRV analysis is performed on 5 min ECG recordings which in e-health monitoring might be unsuitable,due to real-time requirements.Thus,the aim of this study is to evaluate the association between the raw ECG heartbeats and the HRV features to further reduce the number of heart beats required for the HRV estimation enabling real time monitoring.We propose a deep learning based system,specifically a recurrent neural network for the inference of two time domain HRV features: AVNN(the average of all the NN intervals)and THR(instantaneous heart rate).The obtained results suggest that both AVNN and IHR can be accurately inferred from a shorter ECG interval of about 1 min,with a mean error of<5% of the computed HRV features.
机译：心率变异性（HRV）分析越来越成为对自主神经系统进行评估的有希望的标记。HRV的易于推导已经确定了它的普及，在许多研究和临床研究中成功地使用。然而，传统的HRV分析是在5分钟的ECG记录上进行，在电子健康监测中可能是不合适的，由于实时要求，本研究的目的是评估原始ECG心跳与HRV功能之间的关联，以进一步减少数量HRV估计所需的心跳能够实现实时监控。我们提出了一种基于深度的学习系统，特别是一种经常性的神经网络，用于推理两个时域HRV特征：AVNN（所有NN间隔的平均值）和THR（瞬时心脏速率）。获得的结果表明，AVNN和IHR都可以从大约1分钟的短ECG间隔准确推断出来，平均误差<5％的计算HRV Featu res。

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