Schranz Christoph, Halmich Christina, Mayr Sebastian, Heib Dominik P J
Human Motion Analytics, Salzburg Research Forschungsgesellschaft mbH, Salzburg, Austria.
Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, Austria.
Front Netw Physiol. 2024 Jul 19;4:1425871. doi: 10.3389/fnetp.2024.1425871. eCollection 2024.
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.
睡眠，或缺乏睡眠，会对人类生理、认知表现和情绪健康的许多方面产生深远影响。为确保睡眠监测不受干扰，诸如心冲击图（BCG）等非侵入性测量对于持续的真实世界数据采集至关重要。目前对睡眠期间BCG数据的分析仍然具有挑战性，主要是由于信噪比低、身体运动以及个体间和个体内的高度变异性。为了克服这些挑战，这项工作提出了一种新颖的方法，使用监督深度学习设置来改进从BCG测量中提取J峰。所提出的方法包括使用对称且连续的核函数对离散参考心跳事件进行建模，该核函数称为替代信号。深度学习模型从该替代信号中近似检测目标心跳。将具有各种替代信号的所提出方法与来自信号处理和机器学习方法的现有方法进行比较和评估。使用嵌入床垫中的惯性测量单元（IMU）以及用于参考心跳的心电图，在17个晚上收集了BCG数据集，总共134小时。此外，我们首次应用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，在64秒窗口中的平均绝对误差（MAE）为1.1秒，在8秒窗口中的平均绝对误差为1.38秒。此外，结果表明我们的新方法在各种评估指标下检测心跳位置方面优于当前方法。据我们所知，这是第一种使用核函数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用于事件检测的各种事件编码进行系统比较的方法。
