# Determining Obstruction in Endotracheal Tubes Using Physical Respiratory Signals
###### Abstract  
This study proposes a method for determining obstruction of the endotracheal tube (ET) and its degree and location. Respiratory signals were acquired using a three sensor (microphone, pressure, and flow) integrated sensor connector. Obstruction classification involved pre-processing and feature extraction. During pre-processing, one cycle of the respiratory signal was extracted using respiratory cycle extraction and phase segmentation. The signal was then divided into three phases: (i) inspiratory phase, (ii) expiratory phase, and (iii) between both the phases, where the intrapulmonary pressure increased, decreased, and remained constant, respectively. In the feature extraction process, the results were quantified using absolute value average and texture analyses. Artificial ET tubes were fabricated to simulate the presence of foreign substances in the ET tube; they had different degrees of obstruction (0%, 20%, 40%, and 50%) and obstruction positions (Sections 1, 2, and 3). The experiment was performed by connecting the sensor connector and artificial ET tube between the ventilator and test lung. Respiratory signals were obtained in 10 cases by cross connecting the artificial ET tubes. The degree and location of obstruction were classified according to the average absolute value and texture analyses of the flow data. The obstruction can be determined through the texture analysis results using the combined microphone and flow sensor data. The proposed method is simple in configuration, can be readily used in existing setups, and can be operated regardless of surrounding noise. ed 2023 19 March 2023 23 March 2023 202  
Currently, ventilators do not provide any information on the degree of obstruction in the ET tube, and healthcare workers must manually check the degree of obstruction through auscultation at regular intervals or periodically apply suction, regardless of the absence or presence of obstruction [7; 8]. Studies report that, in most hospitals, healthcare workers are required to attend to more than the recommended number of patients [9; 10]. Hence, frequently checking intubated patients is difficult owing to their high workload. Additionally, skilled experts are required to distinguish between normal breathing, obstructive sounds, and breathing noise through auscultation [11; 12]. Recently, a technique of automatically detecting the respiratory state of a patient using a ventilator and an electronic stethoscope [13; 14] and a study to improve the convenience of homecare by linking it with a smart phone [15] have been attempted. Although these studies have presented results on usefulness, utility and applicability, additional research is needed to provide information with high accuracy. Therefore, attempts are being made to develop a technology that automatically provides information on the degree of obstruction of the intubation tube so that suction can be performed only when necessary [16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Most previous studies have conducted breath sound analyses using a microphone. ET tube obstruction was determined by frequency analysis of the patient's breathing sounds using a spectrogram [16; 17; 18]. A study was also conducted to classify normal and abnormal respiration through mel-frequency cepstral coefficient (MFCC) analysis using the breathing sounds of patients with various pathologies [19; 20]. Additionally, studies have been conducted to analyze the presence of obstructions by extracting features through digital signal processing after recording lung sounds through auscultation [21; 22]. Recently, several studies have used machine learning to increase the accuracy of analysis [16; 17; 18; 23], and studies using two sensors, i.e., a microphone and a flow sensor, have also been conducted [24; 25]. However, most of these studies only detected obstruction in the ET tube; the location and degree could not be determined. Moreover, they have insufficient accuracy.