Abstract
A pulse waveform profiling algorithm is introduced for processing real-time photoplethysmography (PPG) data in mobile applications. The real PPG waveform is discriminated from artifacts based on rules of probability distribution and physiological significance. The swift background is removed through a two-step method to minimize its effect on the final waveform analysis: interpolating the midpoint of the amplitude of the PPG AC part and then interpolating the minimum of each pulse period. The pulse waveform features are extracted from the stratified PPG wave data. The implementation of the proposed algorithm in a real-time mobile application demonstrated its high efficiency and robustness with a sensitivity of around 96% and a positive specificity of 100%. The potential applications of these extracted features are discussed in terms of long-term trends in physiology such as circadian and arrhythmia real-time monitoring.
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