Colburn David A M, Chern Terry L, Guo Vincent E, Salamat Kennedy A, Pugliese Daniel N, Bradley Corey K, Shimbo Daichi, Sia Samuel K
Department of Biomedical Engineering, Columbia University, New York, NY 10027 USA.
Department of Computer Science, Columbia University, New York, NY 10027 USA.
NPJ Biosens. 2025;2(1):5. doi: 10.1038/s44328-024-00021-y. Epub 2025 Jan 31.
Cuffless noninvasive blood pressure (BP) measurement could enable early unobtrusive detection of abnormal BP patterns, but when the sensor is placed on a location away from heart level (such as the arm), its accuracy is compromised by variations in the position of the sensor relative to heart level; such positional variations produce hydrostatic pressure changes that can cause swings in tens of mmHg in the measured BP if uncorrected. A standard method to correct for changes in hydrostatic pressure makes use of a bulky fluid-filled tube connecting heart level to the sensor. Here, we present an alternative method to correct for variations in hydrostatic pressure using unobtrusive wearable inertial sensors. This method, called IMU-Track, analyzes motion information with a deep learning model; for sensors placed on the arm, IMU-Track calculates parameterized arm-pose coordinates, which are then used to correct the measured BP. We demonstrated IMU-Track for BP measurements derived from pulse transit time, acquired using electrocardiography and finger photoplethysmography, with validation data collected across 20 participants. Across these participants, for the hand heights of 25 cm below or above the heart, mean absolute errors were reduced for systolic BP from 13.5 ± 1.1 and 9.6 ± 1.1 to 5.9 ± 0.7 and 5.9 ± 0.5 mmHg, respectively, and were reduced for diastolic BP from 15.0 ± 1.0 and 11.5 ± 1.5 to 6.8 ± 0.5 and 7.8 ± 0.8, respectively. On a commercial smartphone, the arm-tracking inference time was ~134 ms, sufficiently fast for real-time hydrostatic pressure correction. This method for correcting hydrostatic pressure may enable accurate passive cuffless BP monitors placed at positions away from heart level that accommodate everyday movements.
无袖带无创血压测量能够早期且不引人注意地检测到异常血压模式，但当传感器放置在远离心脏水平的位置（如手臂）时，其准确性会因传感器相对于心脏水平位置的变化而受到影响；这种位置变化会产生静水压变化，如果不加以校正，可能导致测量血压出现数十毫米汞柱的波动。一种校正静水压变化的标准方法是使用一根连接心脏水平和传感器的粗大充液管。在此，我们提出一种使用不引人注意的可穿戴惯性传感器校正静水压变化的替代方法。这种方法称为IMU-Track，它使用深度学习模型分析运动信息；对于放置在手臂上的传感器，IMU-Track计算参数化的手臂姿势坐标，然后用于校正测量的血压。我们展示了IMU-Track用于从脉搏传输时间得出的血压测量，该时间通过心电图和手指光电容积脉搏波描记法获取，并收集了20名参与者的验证数据。在这些参与者中，对于心脏以下或以上25厘米的手部高度，收缩压的平均绝对误差分别从13.5±1.1和9.6±1.1毫米汞柱降至5.9±0.7和5.9±0.5毫米汞柱，舒张压的平均绝对误差分别从15.0±1.0和11.5±1.5毫米汞柱降至6.8±0.5和7.8±0.8毫米汞柱。在商用智能手机上，手臂跟踪推理时间约为134毫秒，足以实现实时静水压校正。这种校正静水压的方法可能使准确的被动无袖带血压监测仪能够放置在远离心脏水平的位置，以适应日常活动。
