In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.
Without artificial airway though oral, nasal or airway incision, the bi-level positive airway pressure (Bi-PAP) has been widely employed for respiratory patients. In an effort to investigate the therapeutic effects and measures for the respiratory patients under the noninvasive Bi-PAP ventilation, a therapy system model was designed for virtual ventilation experiments. In this system model, it includes a sub-model of noninvasive Bi-PAP respirator, a sub-model of respiratory patient, and a sub-model of the breath circuit and mask. And based on the Matlab Simulink, a simulation platform for the noninvasive Bi-PAP therapy system was developed to conduct the virtual experiments in simulated respiratory patient with no spontaneous breathing (NSB), chronic obstructive pulmonary disease (COPD) and acute respiratory distress syndrome (ARDS). The simulated outputs such as the respiratory flows, pressures, volumes, etc, were collected and compared to the outputs which were obtained in the physical experiments with the active servo lung. By statistically analyzed with SPSS, the results demonstrated that there was no significant difference (P > 0.1) and was in high similarity (R > 0.7) between the data collected in simulations and physical experiments. The therapy system model of noninvasive Bi-PAP is probably applied for simulating the practical clinical experiment, and maybe conveniently applied to study the technology of noninvasive Bi-PAP for clinicians.