A Principal Component Analysis Based Data Fusion Method for Estimation of Respiratory Volume

Impedance plethysmography (IP) is widely used in pulmonary volume measurement in recent years. Previous researches mainly focused on improving respiratory volume measurement accuracy by improving filter performance, electrode configuration, and so on, ignoring the influence of sleep posture changes. To solve this problem, we presented a principal component analysis (PCA)-based data fusion algorithm to minimize the effects of sleep posture changes on pulmonary volume measurement using a new dual-channel IP system. In situ experiments with ten subjects indicated that the PCA-based data fusion method improved the performance with the mean absolute error decreased ~25%. Thus, the novel method potentially achieves a higher sensitivity of the sleep respiratory function diagnosis.