利用深度学习建立心跳事件的替代模型,以改进卡介苗中的 J 峰检测

Christoph Schranz, Christina Halmich, Sebastian Mayr, Dominik P. J. Heib
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引用次数: 0

摘要

睡眠或睡眠不足对人体生理、认知能力和情绪健康的许多方面都有深远影响。为确保不受干扰地监测睡眠,球心电图(BCG)等非侵入性测量对于持续获取真实世界的数据至关重要。目前,对睡眠期间的 BCG 数据进行分析仍具有挑战性,这主要是由于信噪比低、身体运动以及个体间和个体内的高变异性。为了克服这些挑战,本研究提出了一种新方法,利用有监督的深度学习设置来改进 BCG 测量中的 J 峰提取。所提出的方法包括用对称和连续的核函数(称为替代信号)对离散参考心跳事件进行建模。深度学习模型近似于该代理信号,并从中检测出目标心跳。我们将使用各种代理信号的拟议方法与最先进的信号处理和机器学习方法进行了比较和评估。此外,我们首次采用了专门用于比较基于事件的时间序列的评估指标来评估心跳检测的质量。结果表明,与现有方法相比,所提出的方法在心跳估计方面具有更高的准确性,64 秒窗口的 MAE(平均绝对误差)为 1.1 秒,8 秒窗口的 MAE(平均绝对误差)为 1.38 秒。此外,在检测心跳位置方面,我们的新方法在各种评估指标上都优于现有方法。据我们所知,这是第一种使用核对时间事件进行编码的方法,也是第一种使用基于回归的序列到序列模型对事件检测的各种事件编码进行系统比较的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surrogate modelling of heartbeat events for improved J-peak detection in BCG using deep learning
Sleep, or the lack thereof, has far-reaching consequences on many aspects of human physiology, cognitive performance, and emotional wellbeing. To ensure undisturbed sleep monitoring, unobtrusive measurements such as ballistocardiogram (BCG) are essential for sustained, real-world data acquisition. Current analysis of BCG data during sleep remains challenging, mainly due to low signal-to-noise ratio, physical movements, as well as high inter- and intra-individual variability. To overcome these challenges, this work proposes a novel approach to improve J-peak extraction from BCG measurements using a supervised deep learning setup. The proposed method consists of the modeling of the discrete reference heartbeat events with a symmetric and continuous kernel-function, referred to as surrogate signal. Deep learning models approximate this surrogate signal from which the target heartbeats are detected. The proposed method with various surrogate signals is compared and evaluated with state-of-the-art methods from both signal processing and machine learning approaches. The BCG dataset was collected over 17 nights using inertial measurement units (IMUs) embedded in a mattress, together with an ECG for reference heartbeats, for a total of 134 h. Moreover, we apply for the first time an evaluation metric specialized for the comparison of event-based time series to assess the quality of heartbeat detection. The results show that the proposed approach demonstrates superior accuracy in heartbeat estimation compared to existing approaches, with an MAE (mean absolute error) of 1.1 s in 64-s windows and 1.38 s in 8-s windows. Furthermore, it is shown that our novel approach outperforms current methods in detecting the location of heartbeats across various evaluation metrics. To the best of our knowledge, this is the first approach to encode temporal events using kernels and the first systematic comparison of various event encodings for event detection using a regression-based sequence-to-sequence model.
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