Wittek Peter
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:766-769. doi: 10.1109/EMBC.2016.7590814.
Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal. We evaluate the accuracy of the estimation on 78 samples from 65 subjects (40 male, 25 female, age 29±7) with no history of cardiovascular disease. The estimate for systolic blood pressure has a mean error 4.9±4.9 mm Hg, and 4.3±3.7 mm Hg for diastolic blood pressure when using the oximeter-obtained PPG. The same values are 5.1±4.3 mm Hg and 4.6±4.3 mm Hg when using the phone-obtained PPG, comparing with A&D UA-767PBT result as gold standard. The simplicity of the method encourages ambulatory measurement, and given the ease of sharing the measured data, we expect a shift to data-oriented approaches deriving insight from ubiquitous mobile devices that will yield more accurate machine learning models in monitoring blood pressure.
通过光学方法无创测量血压引起了广泛关注，但测量的复杂性和参数调整的难度限制了其应用。我们开发了一种利用光电容积脉搏波描记法（PPG）信号的单点光学记录来估计收缩压和舒张压的方法。该估计是数据驱动的，我们使用自动化机器学习算法而非数学模型。将监督学习与离散小波变换相结合，该方法对PPG波形中的微小不规则性不敏感，因此脉搏血氧仪和智能手机摄像头都可以记录该信号。我们对65名无心血管疾病史的受试者（40名男性，25名女性，年龄29±7岁）的78个样本进行了估计准确性评估。使用血氧仪获取的PPG时，收缩压估计的平均误差为4.9±4.9毫米汞柱，舒张压为4.3±3.7毫米汞柱。使用手机获取的PPG时，与作为金标准的A&D UA - 767PBT结果相比，相同的值分别为5.1±4.3毫米汞柱和4.6±4.3毫米汞柱。该方法的简单性鼓励进行动态测量，并且鉴于测量数据易于共享，我们预计会转向从无处不在的移动设备中获取洞察的面向数据的方法，这将在血压监测中产生更准确的机器学习模型。
