This study aims to propose a multifrequency time-difference algorithm using spectral constraints. Based on the knowledge of tissue spectrum in the imaging domain, the fraction model was used in conjunction with the finite element method (FEM) to approximate a conductivity distribution. Then a frequency independent parameter (volume or area fraction change) was reconstructed which made it possible to simultaneously employ multifrequency time-difference boundary voltage data and then reduce the degrees of freedom of the reconstruction problem. Furthermore, this will alleviate the illness of the EIT inverse problem and lead to a better reconstruction result. The numerical validation results suggested that the proposed time-difference fraction reconstruction algorithm behaved better than traditional damped least squares algorithm (DLS) especially in the noise suppression capability. Moreover, under the condition of low signal-to-noise ratio, the proposed algorithm had a more obvious advantage in reconstructions of targets shape and position. This algorithm provides an efficient way to simultaneously utilize multifrequency measurement data for time-difference EIT, and leads to a more accurate reconstruction result. It may show us a new direction for the development of time-difference EIT algorithms in the case that the tissue spectrums are known.
Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in “respiratory signal quality index” to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects’ breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.