Autoregressive Spectral Analysis on Photoplethysmogram(PPG) to estimate Respiratory Rate.
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Abstract— Abnormal respiratory rate is considered to be a important predictor of conditions like cardiac arrest, tachypnea and hypervolemia. Estimating respiration rate of an individual using non invasive techniques such as by using Photoplethysmogram (PPG) and Electrocardiogram(ECG) have been widely discussed and several methods have been proposed to extract respiratory rate information from these signals. However, maintaining a high degree of accuracy in measuring respiratory rate has been a problem. This paper uses Autoregressive Modeling (AR) technique and digital filters to estimate respiratory rate from the PPG signal revealing that this technique has better results in term of accuracy and computational efficiency.
Keywords—Respiratory Rate; Photoplethysmography; Autoregressive (AR) model.
Respiration rate is one of the main vital signs used in all clinical settings. An increased respiratory rate is an important predictor for