Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques.

Motivation: Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results: We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation: The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact: yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information: Supplementary data are available at Bioinformatics online.

Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.

A novel sputum deposition classification method for mechanically ventilated patients based on the long-short-term memory network (LSTM) method was proposed in this study. A wireless ventilation airflow signals collection system was designed and used in this study. The ventilation airflow signals were collected wirelessly and used for sputum deposition classification. Two hundred sixty data groups from 15 patients in the intensive care unit were compiled and analyzed. A two-layer LSTM framework and 11 features extracted from the airflow signals were used for the model training. The cross-validations were adopted to test the classification performance. The sensitivity, specificity, precision, accuracy, F1 score, and G score were calculated. The proposed method has an accuracy of 84.7 ± 4.1% for sputum and non-sputum deposition classification. Moreover, compared with other classifiers (logistic regression, random forest, naive Bayes, support vector machine, and K-nearest neighbor), the proposed LSTM method is superior. In addition, the other advantages of using ventilation airflow signals for classification are its convenience and low complexity. Intelligent devices such as phones, laptops, or ventilators can be used for data processing and reminding medical staff to perform sputum suction. The proposed method could significantly reduce the workload of medical staff and increase the automation and efficiency of medical care, especially during the COVID-19 pandemic.

Novel assisted cough system based on simulating cough airflow dynamics.

ABSTRACT: Cough is a defensive behavior that protects the respiratory system from infection and clears airway secretions. Cough airflow dynamics have been analyzed by a variety of mathematical and experimental tools. In this paper, the cough airflow dynamics of 42 subjects were obtained and analyzed. An identification model based on piecewise Gauss function for cough airflow dynamics is proposed through the dimensionless method, which could achieve over 90% identification accuracy. Meanwhile, an assisted cough system based on pneumatic flow servo system is presented. The vacuum situation and feedback control are used to increase the simulated peak cough flow rate, which are important for airway secretion clearance and to avoid airway collapse, respectively. The simulated cough peak flow could reach 5 L/s without the external assistance such as manual pressing, patient cooperation and other means. Finally, the backstepping control is developed to generate a simulated cough airflow that closely mimics the natural cough airflow of humans. The assisted cough system opens up wide opportunities of practical application in airway secretion clearance for critically ill patients with COVID 2019 and other pulmonary diseases.

Numerical Analysis of Airway Mucus Clearance Effectiveness Using Assisted Coughing Techniques.

Cough is a protective respiratory reflex used to clear respiratory airway mucus. For patients with cough weakness, such as chronic obstructive pulmonary disease, neuromuscular weakness disease and other respiratory diseases, assisted coughing techniques are essential to help them clear mucus. In this study, the Eulerian wall film model was applied to simulate the coughing clearance process through a computational fluid dynamics methodology. Airway generation 0 to generation 2 based on realistic geometry is considered in this study. To quantify cough effectiveness, cough efficiency was calculated. Moreover, simulations of four different coughing techniques applied for chronic obstructive pulmonary disease and neuromuscular weakness disease were conducted. The influences of mucus film thickness and mucus viscosity on cough efficiency were analyzed. From the simulation results, we found that with increasing mucus film thickness and decreasing mucus viscosity, cough efficiency improved accordingly. Assisted coughing technologies have little influence on the mucus clearance of chronic obstructive pulmonary disease models. Finally, it was observed that the cough efficiency of the mechanical insufflation-exsufflation technique (MIE) is more than 40 times the value of an unassisted coughing technique, which indicates that the MIE technology has a great effect on airway mucus clearance for neuromuscular weakness disease models.

A Novel Method for Automatic Identification of Breathing State.

Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea.

Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.

Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.

Online Estimation Method for Respiratory Parameters Based on a Pneumatic Model.

Mechanical ventilation is an important method to help people breathe. Respiratory parameters of ventilated patients are usually tracked for pulmonary diagnostics and respiratory treatment assessment. In this paper, to improve the estimation accuracy of respiratory parameters, a pneumatic model for mechanical ventilation was proposed. Furthermore, based on the mathematical model, a recursive least-squares algorithm was adopted to estimate the respiratory parameters. Finally, through experimental and numerical study, it was demonstrated that the proposed estimation method was effective and the method can be used in pulmonary diagnostics and treatment.