Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Philips Research, Philips, Eindhoven, The Netherlands.
Physiol Meas. 2024 Mar 21;45(3). doi: 10.1088/1361-6579/ad2f5e.
. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumestimation and a Markov decision process to approach an optimal solution.. The maximumestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.
. 在广泛的临床应用中，对心脏参数进行非侵入性的长期监测非常重要，例如评估急性疾病的严重程度和非侵入性的睡眠监测。在这里，我们确定了佩戴在胸部的加速度计检测心跳的准确性和稳健性。. 我们对两个睡眠中心的 147 名个体（女性 69 名，男性 78 名）进行了整夜记录。比较了两种用于加速度信号中心跳检测的方法：一种是先前描述的基于局部周期性的方法，另一种是新的扩展方法，该方法结合了最大估计和马尔可夫决策过程，以接近最佳解决方案。. 最大估计显著提高了性能，估计两次心跳之间的间隔的平均绝对误差仅为 3.5 毫秒，心率测量的 95%一致性界限为-1.7 至+1.0 次/分钟。该性能在体位变化期间保持不变，仅受到睡眠障碍和人口统计学因素的微弱影响。. 新方法可能使佩戴在胸部的加速度计在各种应用中得到应用，例如在体睡眠分期和住院患者监测中。
