TY - EJOU AU - Sunil, N. Kins Burk AU - Ganesan, R. AU - Sankaragomathi, B. TI - Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features T2 - Computer Modeling in Engineering \& Sciences PY - 2019 VL - 118 IS - 2 SN - 1526-1506 AB - Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal and sleep apnea signal. The proposed method is validated to measure the performance metrics like sensitivity, specificity, accuracy and F1 score by applying time domain and frequency domain features separately. Additionally, the performance of the CART-PSO (CPSO) classification algorithm is evaluated through comparing its measures with existing classification algorithms. Concurrently, the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO. KW - Obstructive sleep apnea KW - photoplethysmogram signal KW - time domain features KW - frequency domain features KW - classification and regression tree classifier KW - particle swarm optimization algorithm DO - 10.31614/cmes.2018.04484