Farooq Umar, Jang Dae-Geun, Park Jang-Ho, Park Seung-Hun
U-Health Lab, Department of Biomedical Engineering, Kyung Hee University, South Korea.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4582-5. doi: 10.1109/IEMBS.2010.5626023.
This paper presents real-time signal processing algorithm for detection of onsets and peaks in Photoplethysmogram (PPG) waveform. Algorithm relies on the analysis of amplitude, slope and inter-beat intervals. The presented algorithm consists of four stages for characterizing PPG waveform. Preprocessing stage involves transformation of PPG since the original waveform is less impulsive and robust. In second stage, algorithm seeks for valid pulse detection in transformed signal complying with the amplitude threshold and inter-beat interval. On detection of valid pulses, algorithm then searches backward and forward in transformed signal for the detection of peaks and onsets. Further the detection parameters are made adaptive to comply with varying beat morphologies and fluctuations in baseline. All signal processing steps and decision logics are implemented with low computational complexity to make it applicable for compact ubiquitous health monitoring devices. On evaluation with our database, the algorithm achieved sensitivity of 96.89% and positive predictivity of 94.55% within an acceptance level of 12 ms.
本文提出了一种用于检测光电容积脉搏波（PPG）波形中起始点和峰值的实时信号处理算法。该算法依赖于对幅度、斜率和心搏间期的分析。所提出的算法包括四个阶段来表征PPG波形。预处理阶段涉及对PPG进行变换，因为原始波形的脉冲性和鲁棒性较差。在第二阶段，算法在变换后的信号中寻找符合幅度阈值和心搏间期的有效脉搏检测。在检测到有效脉搏后，算法然后在变换后的信号中向前和向后搜索以检测峰值和起始点。此外，检测参数被设为自适应的，以适应不同的搏动形态和基线波动。所有信号处理步骤和决策逻辑都以低计算复杂度实现，使其适用于紧凑的普及型健康监测设备。在我们的数据库上进行评估时，该算法在12毫秒的接受水平内实现了96.89%的灵敏度和94.55%的阳性预测值。
