A Study of Breathing Sound-based Classification of Patients With Breathing Disorders

Study Overview
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Brief Summary
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Sleep-disordered breathing can damage the cardiovascular system, and may also lead to dysregulation of the autonomic nervous system, endocrine disorders, and hemodynamic changes, causing multi-system and multi-organ damage. Screening for potential central-type patients among patients with respiratory disorders can help provide scientific diagnosis and treatment decisions, thus achieving precise treatment. Currently, research on the identification of sleep-disordered breathing phenotypes is in its infancy. Sleep-disordered breathing phenotypes, such as obstructive and central respiratory events, vary widely among individuals. Compared to indirect methods such as RIP and SpO2, changes in breathing sounds and snoring during sleep can more directly reflect airway obstruction. Different types of sleep-disordered breathing exhibit different characteristics in terms of snoring. Patients with obstructive sleep apnea experience narrowing or blockage of the airway due to relaxation of the throat muscles during sleep, which leads to breathing pauses and hypopnea events, resulting in decreased blood oxygen levels, arousal, and snoring. Central sleep apnea is caused by problems with the brainstem or respiratory control center, leading to breathing pauses. Snoring is usually not very prominent in patients with central sleep apnea. This study aims to screen for potential central-type patients by analyzing upper airway sounds of patients with sleep-disordered breathing, in order to achieve precise treatment.

Detailed Description
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Screening for central apnea from obstructive apnea is important for the precise treatment of respiratory disorders. Based on the above assumptions that the time domain and acoustic variability of respiratory sound signals contain key information about the degree of upper respiratory tract obstruction and the role of respiratory effort, this study proposes a sleep breathing disorder category identification model based on respiratory sound analysis. A microphone device and sound card are used to capture the patient's audio signal overnight and transmit it to the Raspberry Pi for processing and storage. The microphone device is worn at the neckline of the patient to collect the sound signal of breathing, which ensures that the sound signal is less affected by the sleeping position. Sleep and wakefulness are then separated from breathing sound signals throughout the night and the patient's sleep period is analyzed individually. The apnea location is determined in 30s frames, and in apnea event detection, if the sound stops and lasts for more than 10 seconds, it may be a apnea event. Taking the sound signal of 20s to 30s before apnea as the analysis object, the OpenSmile and Tsfresh feature extraction tools are used to extract acoustic features and envelope features, respectively. The acoustic signature reflects the frequency domain information of apnea, and the envelope feature reflects the time domain signature of apnea. Fusion of acoustic and envelope features enables analysis of airway obstruction and respiratory effort in patients with respiratory disorders. Finally, a machine learning model is established using acoustic features and envelope features as inputs, and each apnea event is classified one by one. In this study, two centers are included, namely the Sleep Therapy Center of the First People's Hospital of Huai'an and the Sleep Therapy Center of the Jiangsu Provincial People's Hospital. Sleep audio data for 167 and 62 cases are expected to be included. The training and validation sets used for modeling are 90 cases, using ten-fold cross-validation, the internal test set is expected to include 77 sleep audio data, and the audio data of 62 patients collected from Jiangsu Provincial People's Hospital are used as the external test set.

Official Title
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Huai'an First People's Hospital

Conditions
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Apnea

Intervention / Treatment
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* Diagnostic Test: polysomnography


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: the age of the patient is 18-75 years old; patients with confirmed PSG with AHI ≥ 5 times/hour, with or without daytime sleepiness, hypertension, and diabetes; sleep-disordered breathing has not been treated; informed consent of patients Exclusion Criteria: pregnancy; have other diseases that are not suitable for participation in this study

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Group 1<br>Patients suspected of having obstructive apnea | Diagnostic Test: polysomnography<br>* Polysomnography is mainly used to diagnose sleep breathing disorders, including sleep apnea syndrome, snoring, upper airway resistance syndrome, and also used for the auxiliary diagnosis of other sleep disorders, such as: narcolepsy, restless legs syndrome, insomnia classification, etc.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy of the binary classification of obstructive apnea and central apnea | Based on the binary classification of events, obstructive apnea is the negative class and central apnea is the positive class. Accuracy is the ratio of the predicted correct positive plus negative class to the total event. | 3 days | 
| The recall of the binary classification of obstructive apnea and central apnea | According to the binary classification of events, obstructive apnea is negative and central apnea is positive. Recall represents the proportion of all positive events in the dataset that the model correctly classifies as positive. | 3 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy of the patient's apnea detection | Apnea detection in 30-second segments. Segments with apnea are positive and segments without apnea are negative. Accuracy is the ratio of the predicted correct positive plus negative classes to the total fragment. | 3 days | 
| The recall of the patient's apnea detection. | 30-second apnea detection. Segments with apnea are positive and those without apnea are negative. Recall indicates the ratio of the correctly classified positive fragments of the model to all positive fragments in the dataset. | 3 days | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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snoring sounds, respiratory sounds, obstructive apnea, central apnea, heart failure