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Using signal processing techniques to predict PPG for T2D

Gerald C Hsu

 The author has collected an entire set of PPG and lifestyle data for a period of 994 days with 2,982 meals (6/11/2015-3/1/2018). This paper discusses the methodology and accuracy of his developed PPG prediction model using signal processing techniques for type 2 diabetes. Materials & Methods: thanks to his academic background in mathematics, physics, and engineering, he views these biomedical and lifestyle data as a set of nonlinear signal waves. He applied signal processing to decompose this time-series measured PPG signal into multiple (>10 lifestyle factors) single-sourced composite waveforms, examined each composite signal, then recombined them into a predicted PPG curve. Finally, he compared this predicted signal against the measured signal to calculate its accuracy and correlation. He further improved his model via a trial-and-error “curve-fitting” method. Results: The PPG’s major creation source, corresponding glucose, and contribution level are as follows: Carbs/Sugar: 14.5 mg/ dL, 37%; Post-meal Exercise-15.7 mg/dL, 41%; Weather 3.8 mg/dL, 10%; Measurement delay -2.4 mg/dL, 7% et al. -1.9 mg/dL, 5%. During this era , his average PPG values are: Predicted 119.16 mg/dL Measured 119.88 mg/dL with 99.4% linear accuracy and a high correlation of 70%. Conclusion: The quantitative results from the developed PPG prediction model reflect the accuracy and applicability for type 2 diabetes control via a guided lifestyle management. The use of signal processing from electronics engineering and computing is additionally proven quite effective for this investigation.
Regardless the argument on glucose testing method’s accuracy via either lab-tested A1C or finger piercing and testing strips, the author has collected a complete set of PPG data using lab-tested A1C and finger prick testing strips plus his created lifestyle data during a period of 1,075 days with 3,225 meals (6/1/2015 - 5/11/2018). This PPG-related data set, size of ~400,000 data, is only a small portion of his entire ~1.5 million data. Due to his mathematics and engineering background, he views these data curves related to biomedical conditions and lifestyle management as a collection of various nonlinear input and output signal waves of the human body. At first, he applied “Finite Element Method” of engineering modeling to convert this “analog” human system into a “digitized” mathematical system in order to get an approximate solution of the real human system. He sees each digitized sub-wave as representing a single-source created contribution element of the PPG wave. Therefore, he applied signal processing techniques to decompose this measured PPG signal into more than 10 single-sourced sub-waves. He carefully checked each sub-signal waveform for its completeness, accuracy, and correlation with other curves, using time-series, spatial, and frequency domain analyses, etc. Over the past three years, he continuously explored and added some missing influential factors into the formation of the PPG signal. His purpose was trying to improve the predicted PPG waveform’s contents and accuracy while maintaining high correlation with the measured PPG waveform. For example, by the fall of 2016, the accuracy of his predicted PPG reached ~95%. In September of 2017, he identified that weather temperature also had an impact on glucose value. Therefore, he selected a 2-year period (6/2015 - 7/2017) to examine his travel schedule in detail and also entered each day’s local ambient temperature of the city where he stayed. In this way, he was able to generate a new temperature sub-wave which brought the accuracy of the predicted PPG from ~95% to ~98%.