Indian Institute of Technology Madras, India.
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Germany.
Stud Health Technol Inform. 2024 Aug 22;316:988-992. doi: 10.3233/SHTI240576.
Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.
在驾驶环境中对生理信号（如心电图（ECG））进行连续监测，通过提供心血管健康的实时信息，有可能减少频繁体检的需求。然而，由于运动伪影、噪声和丢包，从安装在方向盘上的传感器中捕获 ECG 会带来困难。为了解决这个问题，我们提出了一种使用双向长短期记忆（BiLSTM）模型进行传感器融合的可靠而准确的心跳检测新方法。我们的数据集包含参考 ECG、方向盘 ECG、光体积描记图（PPG）和成像 PPG（iPPG）信号，这些信号在驾驶场景中更易于捕获。我们结合这些信号进行 R 波检测。我们使用单个信号和信号融合技术进行实验，以评估检测到的心跳位置的性能。BiLSTM 模型在城市驾驶场景中的性能达到 62.69%。该模型可以集成到系统中，以检测心跳位置，进行进一步分析。
