Analysis of Breath Sounds During Surgery
Conditions: General Anesthesia, Intraoperative Monitoring, Respiratory Sounds
Sponsor: Taipei Veterans General Hospital, Taiwan

Analysis of Breath Sounds During Surgery 
 
NCT Number: Not yet assigned 
Unique Protocol ID: 2022-09-002AC 
Sponsor: Taipei Veterans General Hospital, Taiwan 
Principal Investigator: Hui-Hsuan Ke, MD 
Document Type: Study Protocol and Statistical Analysis Plan (SAP) 
Version: 1.0 
Document Date: August 1, 2025 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

1. Background and Rationale 
Auscultation is the practice of listening to physiological sounds using the ear or a 
stethoscope to assess cardiac and respiratory function. Conventional auscultation during 
anesthesia relies heavily on anesthesiologists' experience and is subjective, intermittent, 
and vulnerable to noise and surgical constraints. 
Prior studies have shown that breath-sound–based monitoring may provide useful 
information on airway patency, ventilatory adequacy, and endotracheal tube positioning. 
However, the lack of continuous, objective, and standardized intraoperative breath 
sound monitoring limits timely detection of abnormal respiratory conditions. 
Electronic stethoscope patches enable continuous, high‑fidelity waveform capture and 
signal processing. Despite promising technology, large real‑world intraoperative datasets 
remain scarce. This study aims to systematically collect breath sound waveforms and 
clinical respiratory events during general anesthesia to evaluate feasibility, accuracy, 
and potential clinical applications. 
 
2. Study Objectives 
Primary Objective: 
 Evaluate the accuracy and agreement of breath-sound–based respiratory monitoring 
compared with clinical respiratory assessment. 
 
Secondary Objectives: 
 Analyze the correlation between acoustic features and intraoperative respiratory 
events. 
 Explore feasibility and interpretability of real‑time spectrograms and waveform 
characteristics for respiratory assessment. 
 Evaluate user experience and workflow impact using a structured 5‑point Likert scale. 

 Develop and test signal‑processing algorithms for identifying abnormal acoustic 
patterns (e.g., wheeze-like signals, amplitude reduction, spectral shift). 
 
3. Study Design 
This is a single-center, prospective, observational study conducted in operating rooms at 
Taipei Veterans General Hospital. Approximately 30 adult patients undergoing elective 
surgery under general anesthesia will be enrolled. This study does not involve 
randomization or blinding. Breath sound monitoring is non‑invasive and observational; all 
anesthetic and surgical procedures follow standard clinical practice. 
 
4. Study Procedures and Methods 
4.1 Study Flow 
1. Informed consent obtained preoperatively. 
2. Routine physiologic monitors applied before induction. 
3. Adhesive electronic stethoscope patches placed on the chest wall for baseline 
recordings. 
4. General anesthesia induction, intubation, and surgery performed per standard care. 
5. Continuous breath sound waveform collection throughout anesthesia (from induction 
to emergence). 
6. Documentation of ventilator settings, clinical respiratory observations, and predefined 
respiratory events. 
7. Removal of sensors at the end of surgery; patient transferred to recovery room. 
8. Data extraction and offline analysis. 
 
4.2 Device Placement 
Two to four adhesive electronic stethoscope patches will be placed at the following 
predefined anatomical locations: 

 Left 2nd intercostal space 
 Right 2nd intercostal space 
 Left anterior axillary line at the 5th intercostal space 
 Right anterior axillary line at the 5th intercostal space 
 
4.3 Data Acquisition 
 Sampling rate: 8 kHz 
 Format: raw waveform files (.wav) and exported .csv files 
 Continuous recording from induction until emergence 
 
4.4 Clinical Event Logging 
Documented by anesthesiologists: 
 Hypoventilation 
 Airway obstruction 
 Increased airway pressure 
 Wheeze-like acoustic patterns 
 Secretion accumulation requiring suction 
 Endotracheal tube dislodgement/repositioning 
 
4.5 Clinical Observations 
Routine anesthetic and surgical care. If the sensor detaches, it will be repositioned. No 
intervention is dictated by study protocol. 
 
5. Eligibility Criteria 
Inclusion Criteria: 
 Age ≥ 20 years 
 ASA physical status I–III 

 Scheduled for elective surgery requiring general anesthesia 
 Able and willing to provide informed consent 
 
Exclusion Criteria: 
 Severe chronic pulmonary disease 
 Severe chest wall deformity 
 Skin allergy to adhesives or damaged skin at patch location 
 Refusal or inability to participate 
 
6. Outcome Measures 
Primary Outcome Measure: 
1. Accuracy of electronic breath sound monitoring during surgery 
Assessment will be based on the proportion of intraoperative respiratory events 
correctly identified by the electronic breath sound monitoring system compared with 
standard clinical assessment. 
Time Frame: During surgery (from induction of anesthesia until emergence) 
 
Secondary Outcome Measures: 
2. Incidence of respiratory events detected by the device 
Number of intraoperative respiratory events (e.g., hypoventilation, airway obstruction, 
disconnection) identified by the monitoring system. 
Time Frame: Intraoperative period 
 
3. Correlation between electronic breath sound monitoring and clinical observation 
Correlation (r-value) between device output and anesthesiologist-documented 
respiratory findings. 
Time Frame: Intraoperative period 

 
4. Feasibility of device use during anesthesia 
Feasibility will be assessed by reporting the proportion of cases in which continuous 
breath sound monitoring was successfully completed without interruption, including 
documentation of signal quality issues and workflow interference if present. 
(No rating scale will be used.) 
Time Frame: Postoperative evaluation (immediately after surgery) 
 
7. Sample Size Justification 
A total of 30 subjects is sufficient for feasibility, pilot signal characterization, algorithm 
development, and effect‑size estimation for future powered studies. This sample size is 
consistent with similar pilot studies on electronic auscultation and perioperative 
respiratory monitoring. 
 
8. Statistical Analysis Plan (SAP) 
8.1 Signal Processing 
 Bandpass filtering (100–2000 Hz) 
 FFT‑based spectral analysis 
 Spectrogram generation (Short‑Time Fourier Transform) 
 Extraction of power, amplitude, frequency peaks, and spectral ratios 
 
8.2 Event Detection Performance 
 Sensitivity, specificity, PPV, NPV 
 ROC curves and AUC 
 Confusion matrix comparing algorithm detection vs. clinician assessment 
 
8.3 Agreement Analysis 

 Bland–Altman analysis comparing acoustic‑derived respiratory metrics with clinical 
reference values 
 
8.4 Correlation and Paired Tests 
 Spearman correlation between acoustic features and ventilatory parameters 
 Paired t‑test and Wilcoxon signed‑rank test for within‑subject comparisons 
All analyses performed using validated statistical packages in Python or R. 
 
9. Adverse Event Management 
No adverse events are expected. Adhesive patches are non‑invasive and pose minimal 
risk. If discomfort occurs, subjects may contact the provided 24‑hour emergency number. 
Clinical evaluation will be arranged if needed. Participants may withdraw at any time 
without affecting their medical care. 
 
10. Subject Rights and Safety Protections 
Participants are protected under institutional and national human‑subject research 
policies. Informed consent will be obtained from all participants. All collected data will be 
de‑identified. Only authorized research personnel may access raw data. 
 
11. Data Management and IPD Sharing 
Breath sound waveforms and annotations will be stored securely on institutional servers. 
De‑identified datasets may be shared upon request beginning 6 months after study 
completion. No identifiable information will be released. 
 
12. References 
1. 
Boriosi JP et al. Utility of pretracheal stethoscope in detecting ventilatory 
abnormalities during propofol sedation. Paediatr Anaesth. 2019. 

2. 
Ouchi K et al. Acoustic respiratory rate monitoring in intravenous anesthesia. J Clin 
Monit Comput. 2017. 
3. 
Wei TJ et al. Electronic auscultation for ventilation status in PPE. Front Med. 2022. 
4. 
Mansy HA et al. Breath sound changes with malpositioned endotracheal tubes. 
Med Biol Eng Comput. 2005. 
5. 
Manecke GR et al. Waveform and spectral characteristics of breath sounds under 
general anesthesia. Int J Clin Monit Comput. 1997.