Analysis of Breath Sounds During Surgery

Overview

This study aims to analyze breath sounds during surgery by using electronic stethoscope sensors attached to patients under general anesthesia. The study will evaluate whether breath sound monitoring can provide useful information for respiratory management, assist anesthesiologists in early detection of abnormal breathing events, and support safer perioperative care. A total of 30 adult patients undergoing elective surgery under general anesthesia will be enrolled.

Continuous breath sound monitoring has the potential to provide anesthesiologists with real-time physiological information that is not captured by standard perioperative monitors. During general anesthesia, subtle changes in airflow or airway patency may precede clinically apparent respiratory compromise. Traditional auscultation is intermittent, subjective, and dependent on the operator's experience. Recent advances in electronic stethoscope technology enable continuous, high-fidelity acquisition of breath sound signals, allowing quantitative analysis and automated detection of abnormal respiratory patterns. In this prospective observational study, adult patients undergoing elective surgery under general anesthesia will have electronic stethoscope sensors placed on the anterior chest wall prior to induction. Breath sound signals will be continuously recorded throughout anesthesia and surgery. Signal acquisition parameters (sampling rate, filtering, and sensor placement) will be standardized across participants. All breath sound recordings will be synchronized with standard intraoperative monitoring data, including respiratory rate, tidal volume, capnography, pulse oximetry, and hemodynamic parameters. The synchronized dataset will allow temporal correlation between acoustic features and clinically documented respiratory events. Collected signals will undergo post-processing to extract quantitative acoustic features such as amplitude, frequency distribution, airflow-related patterns, and event-associated spectral changes. Episodes suggestive of abnormal respiration-such as diminished breath sounds, irregular airflow patterns, obstruction-like signatures, or apnea-like silent periods-will be identified and compared with clinical observations recorded by anesthesia providers. The study will also evaluate the feasibility of integrating continuous breath sound information into perioperative workflows, including the practicality of sensor placement, stability of recordings during surgical manipulation, and compatibility with existing monitoring systems. The overall goal of the study is to generate foundational evidence on the technical feasibility and clinical relevance of continuous breath sound monitoring under general anesthesia. Findings from this study may support future development of automated respiratory event detection tools and may contribute to safer perioperative respiratory management.