Performance of Frequency Translation in Separating a Photoplethysmographic Signal from an Additive Motion Artifact

Obtaining red and infrared (IR) photoplethysmo-graphic (PPG) signals by a general method in a commercial pulse oximeter is overlapped with an additive motion artifact (MA) signal routinely. When the red and IR PPG signals are combined with the MA signal, a percentage of oxygen saturation (SpO2) is unreliable. To prevent the overlapping problem, a technique of frequency translation is introduced to shift the PPG frequency components away from the MA frequency components. The introduced approach remodels an LED-driving system by substituting an alternating current (AC) source for a traditional direct current (DC) source in the commercial pulse oximeter. To assess the performance, the SpO2 values computed from the red and IR PPG signals acquired by the presented solution are evaluated when four natural poses of motion occur. Besides, the well-known methods are used to calculate the SpO2 values from the red and IR PPG signals sensed by the conventional LED-emitting system during motion for the efficient comparison. The well-known methods are discrete saturation transform (DST), fast independent component analysis (fICA) and compression of Fourier coefficients (CFC). The resulting SpO2 values show that the technique of frequency translation provides overall mean error lower than the selected schemes for all postures. The overall mean error of the introduced technique is 1.1% while the approaches of DST, fICA and CFC yield the overall mean errors by 2.9%, 15.4% and 8.4%, respectively.

[1]  Sune Duun,et al.  Independent component analysis applied to pulse oximetry in the estimation of the arterial oxygen saturation (SpO2) - a comparative study , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Ronald M. Aarts,et al.  Reduction of Periodic Motion Artifacts in Photoplethysmography , 2017, IEEE Transactions on Biomedical Engineering.

[3]  John Leis Communication Systems Principles Using MATLAB , 2018 .

[4]  Jo Woon Chong,et al.  Photoplethysmograph Signal Reconstruction based on a Novel Motion Artifact Detection-Reduction Approach. Part II: Motion and Noise Artifact Removal , 2014, Annals of Biomedical Engineering.

[5]  Hao Zhang,et al.  A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion , 2017, Comput. Biol. Medicine.

[6]  Jiang Wu,et al.  A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals , 2017, Comput. Math. Methods Medicine.

[7]  K. Ashoka Reddy,et al.  Use of Fourier Series Analysis for Motion Artifact Reduction and Data Compression of Photoplethysmographic Signals , 2009, IEEE Transactions on Instrumentation and Measurement.

[8]  K. Ashoka Reddy,et al.  Utilization of adaptive-coefficient estimation method for Motion artifacts reduction from photoplethysmographic signals , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[9]  Donggeon Han,et al.  A flexible organic reflectance oximeter array , 2018, Proceedings of the National Academy of Sciences.

[10]  佐古 曜一郎,et al.  Signal processing apparatus, signal processing method , 2008 .

[11]  Jeerasuda Koseeyaporn,et al.  A Photoplethysmographic Signal Isolated From an Additive Motion Artifact by Frequency Translation , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[12]  K. Ashoka Reddy,et al.  Reduction of motion artifacts from Pulse oximeter's PPG signals using MSICA , 2016, 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).