Healthc Inform Res.  2015 Apr;21(2):111-117. 10.4258/hir.2015.21.2.111.

Wave Detection in Acceleration Plethysmogram

• Ahn JM

• KMID: 2284614
• DOI: http://doi.org/10.4258/hir.2015.21.2.111

Abstract

OBJECTIVES
Acceleration plethysmogram (APG) obtained from the second derivative of photoplethysmography (PPG) is used to predict risk factors for atherosclerosis with age. This technique is promising for early screening of atherosclerotic pathologies. However, extraction of the wave indices of APG signals measured from the fingertip is challenging. In this paper, the development of a wave detection algorithm including a preamplifier based on a microcontroller that can detect the a, b, c, and d wave indices is proposed.
METHODS
The 4th order derivative of a PPG under real measurements of an APG waveform was introduced to clearly separate the components of the waveform, and to improve the rate of successful wave detection. A preamplifier with a Sallen-Key low pass filter and a wave detection algorithm with programmable gain control, mathematical differentials, and a digital IIR notch filter were designed.
RESULTS
The frequency response of the digital IIR filter was evaluated, and a pulse train consisting of a specific area in which the wave indices existed was generated. The programmable gain control maintained a constant APG amplitude at the output for varying PPG amplitudes. For 164 subjects, the mean values and standard deviation of the a wave index corresponding to the magnitude of the APG signal were 1,106.45 and +/-47.75, respectively.
CONCLUSIONS
We conclude that the proposed algorithm and preamplifier designed to extract the wave indices of an APG in real-time are useful for evaluating vascular aging in the cardiovascular system in a simple healthcare device.

Keyword

Acceleration Plethysmogram; Arterial Stiffness; Healthcare; Vascular Aging; Photoplethysmography

MeSH Terms

Acceleration*
Aging
Atherosclerosis
Cardiovascular System
Delivery of Health Care
Mass Screening
Pathology
Photoplethysmography
Risk Factors
Vascular Stiffness

Figure

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  Figure 1 System configuration based on a microcontroller. An analog to digital converter, digital to analog converter, and pulse width modulation (PWM) timer provided by the microcontroller were used to generate the photoplethysmography and to develop a wave detection algorithm. LED: light emitting diode, HPF: high pass filter, LPF: low pass filter.

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  Figure 2 A 1st order high pass filter with a cutoff frequency of 0.31 Hz and a fixed gain of 511. AD converter: analog to digital converter.

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  Figure 3 A 2nd order active Sallen-Key low pass filter with a gain of unity.

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  Figure 4 Flowchart of the wave detection algorithm, which consisted of two stages: gain control regulating the output peak of the 1st derivative when the APG amplitude is higher or lower than a predetermined level and a pulse train determining the areas in which the wave indices existed based on the 4th derivative analysis. PPG: photoplethysmography, APG: acceleration plethysmogram.

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  Figure 5 Fingertip photoplethysmogram signal measurement; (top) fingertip photoplethysmogram, and (middle) first derivative wave and (bottom) second derivative wave of the photoplethysmogram.

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  Figure 6 PWM LED drive using a PNP transistor for current control, an RC filter, and a buffer on the receiver. PWM: pulse width modulation, LED: light emitting diode.

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  Figure 7 Magnitude response (top) and phase response (bottom) of the IIR 60 Hz notch filter.

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  Figure 8 Two beats photoplethysmography (PPG) signal with 60 Hz power line interference (top) and the IIR filtered PPG (bottom).

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  Figure 9 Acceleration plethysmogram (APG) signal and its pulse train. The S, A, and B areas of the pulse train correspond to the b, c, and d wave indices, respectively.

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  Figure 10 A box-and-whisker plot summarizing all of the a peaks.

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