用深度学习对心跳事件进行替代建模,以改善BCG中的J峰检测,使用深度学习
Christoph Schranz1, Christina Halmich1,2, Sebastian Mayr1
1Human Motion Analytics, Salzburg Research Forschungsgesellschaft mbH, Salzburg, Austria.
Frontiers in network physiology
|August 5, 2024
概括
这项研究引入了一种新的深度学习方法,用于在睡眠期间从弹性心电图 (BCG) 信号中准确检测心跳. 该方法通过增强J峰值提取来改善睡眠监测,克服噪音和运动器件等挑战.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 睡眠质量显著影响整体健康和福祉.
- 弹性心脏图 (BCG) 提供了不引人注目的睡眠监测,但由于信号噪声比和可变性较低,在数据分析方面面临挑战.
- 准确的心跳检测对于可靠的BCG信号睡眠分析至关重要.
研究的目的:
- 开发和评估一种新的深度学习方法,以改善睡眠期间BCG信号中的J峰提取.
- 为了提高心跳检测的准确性和可靠性,以进行不引人注目的睡眠监测.
- 将拟议的方法与现有的信号处理和机器学习技术进行比较.
主要方法:
- 使用监督深度学习框架,使用连续的内核功能替代信号来建模心跳事件.
- 深度学习模型近似替代信号来检测目标心跳.
- 拟议的方法被评估在一个134小时的数据集收集了17个晚上使用床嵌入式IMU和心电图作为参考,使用一个专门的基于事件的时间序列评估指标.
主要成果:
- 这种新型深度学习方法在心跳估计方面表现出比最先进的方法更高的精度.
- 平均绝对误差 (MAE) 达到1.1秒 (64秒窗口) 和1.38秒 (8秒窗口).
- 该方法在心跳位置检测的各种评估指标中超过了现有的方法.
结论:
- 拟议的基于内核的深度学习方法在BCG信号的J峰值提取方面取得了重大进展.
- 这种方法提供了更准确和可靠的心跳检测,对于有效的睡眠监测至关重要.
- 这项工作代表了对时间事件的内核编码在基于回归的序列对序列模型中首次用于事件检测.
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