# Determining Obstruction in Endotracheal Tubes Using Physical Respiratory Signals
## 4 Discussion  
This study aimed to design a method for determining the presence, degree, and location of obstruction in an ET tube used for ET intubation. A sensor connector was fabricated to measure the signals of respiratory sound, pressure, and flow generated by respiration using three sensors, and a simulation experiment was conducted using medical devices. The presence, degree, and location of obstruction were determined through texture analysis using the combined microphone and flow sensor data. The degree and location of obstruction could be determined through absolute average and texture analyses of flow rate data. Therefore, it was possible to determine all cases by texture analysis, and cross-validation of degree and location was possible by absolute average analysis. Most previous studies have focused solely on determining the presence of an obstruction. Recently, AI techniques, such as machine learning or deep learning, have been used to increase accuracy [10; 11; 12]. AI requires complex algorithms and high-performance hardware for computation, as well as a large amount of reliable data to ensure high accuracy. The method in this study has the following three characteristics. First, the sensor connector can be configured using commercially available microphones, pressure sensors, and flow sensors. As it uses a general signal processing method, involving only Phase C of the respiratory cycle defined in this study, it can be implemented with general-purpose hardware. The connector was manufactured as an adapter-type connector, which can be connected between the ventilator and the ET tube, ensuring its easy use. Second, the method enables respiratory signal analysis even in the presence of external noise. Most previous studies primarily used microphones, which are vulnerable to ambient noise. As intubated patients tend to be critically ill, they are likely to require various medical devices with mechanical operation sounds and electrical alarms operating at the same time. As this study primarily used changes in pressure and flow rate due to respiration, the influence of ambient noise was minimal. Third, the method developed in this study provided information on not only the presence of obstruction but also the degree and location of such an obstruction. In Section 1, the absolute average and average value of texture analysis increased as the degree of obstruction increased, whereas these values decreased in Section 3. The average value may have increased due to an increase in the velocity of the flow as the degree of obstruction in Section 1 increased which is located closer to the sensor connector. In Section 3, as the flow moved a distance of 20 cm while passing through Sections 1 and 2, the flow velocity further decreased, and the flow rate value becomes decreased. The skewness and kurtosis of the texture analysis showed trends opposite to those of the absolute average analysis. Skewness is an indicator of the asymmetry of a probability distribution. As the average increased, the skewness decreased as the distribution changed. Kurtosis is a measure of the frequency of the tail compared to the center of the distribution. As the degree of obstruction increased, the kurtosis decreased as the frequency spectrum expanded with noise. Such characteristics may have contributed to the opposing results. Therefore, the degree and location of obstruction can be mutually verified through the results of the absolute average and texture analyses. Section 2, the middle part of the ET tube, did not show any linearly