Why the study?
Does a wavelet decomposition algorithm accurately extract respiratory activity from PPG signals compared to a dedicated respiratory sensor?
Does a wavelet decomposition algorithm accurately extract respiratory activity from PPG signals compared to a dedicated respiratory sensor?
An algorithm using wavelet decomposition can extract respiratory activity from PPG signals with up to 90% correlation to dedicated respiratory sensors, potentially reducing sensor burden and cost.
Should not yet change respiratory monitoring practice; leaves open prospective validation of PPG-based extraction to reduce sensor burden.
Photoplethysmograph is a simple and cost effective method to assess cardiovascular related parameters such as heart rate, arterial blood oxygen saturation, BP the fact that the Photoplethysmograph (PPG) signal caries respiratory information. Earlier physicians used to insert separate sensors to assess respiration and heart rate. In this present work, an efficient algorithm is presented, based on the wavelet decomposition technique to extract the respiratory activity from the PPG signals. With this aim displaying of heart rate and the extraction of respiratory activity is done. Here MATLAB and MULTISIM tools were used for simulation. Wavelet decomposition performed exceptionally well for extraction of respiratory activity from PPG. Extracted signal is compared with a signal which was acquired from the existing respiratory sensor and the correlation between them is up to 90%. Then the obtained parameters are sent to the physician's mobile phone for faster and better assessment of the patient from remote places. Hence using this method it can reduce the usage of sensor for respiration, and also reduces the discomfort caused to the patient, also cost effective.
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PS et al. (2015) studied this question.
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