TITLE:
Optimization of Operating Room Monitor Alarm Sounds Based on Intelligent Audio Processing
AUTHORS:
Yi Liu, Jing Wang, Xiangying Pi, Zumei Gao, Qian Sun
KEYWORDS:
Operating Room, Alarm Fatigue, Psychoacoustics, Deep Learning, CNN + LSTM, Medical Alarm Optimization, Artificial Intelligence in Healthcare, Adaptive Sound Processing
JOURNAL NAME:
Open Journal of Acoustics,
Vol.13 No.2,
June
17,
2025
ABSTRACT: Background: Operating rooms (ORs) are high-noise environments where frequent monitoring of alarms contributes to alarm fatigue, reducing healthcare providers’ response efficiency and potentially compromising patient safety. Traditional alarm systems rely on preset thresholds without intelligent optimization, leading to frequent false alarms and cognitive overload for surgical teams. Recent advances in artificial intelligence (AI) and psychoacoustics offer new opportunities to enhance alarm perception and improve response times. Objective: This study aims to optimize monitor alarm sounds in the OR by integrating deep learning-based alarm classification (CNN + LSTM) with psychoacoustic modeling to reduce auditory fatigue and improve response efficiency. Methods: A total of 35 OR healthcare professionals (15 anesthesiologists, 12 surgeons, 8 nurses) were recruited from a tertiary hospital using purposive sampling. Participants were randomly assigned to a control group (standard alarms) or an experimental group (optimized alarms). Alarm Sound Processing & Classification: Real-world OR alarm sounds (ECG, SpO₂, ventilators, infusion pumps) were recorded and analyzed. Psychoacoustic parameters (loudness, sharpness, unpleasantness index) were computed using Zwicker’s model, Aures formula, and Glasberg & Moore approach. A CNN+LSTM model was trained using Mel-Frequency Cepstral Coefficients (MFCCs) to classify alarms into critical, non-critical, and false alarms. Data were split into training (70%), validation (15%), and testing (15%) sets. Experimental Design & Evaluation: Participants completed two OR scenarios: one using traditional alarms and the other using optimized alarms. Response times, alarm recognition accuracy, and subjective fatigue levels were measured. Fatigue was assessed using NASA Task Load Index (NASA-TLX), Mental Fatigue Scale (MFS), and a Likert-based fatigue rating (1 - 7 scale). Post-hoc power analysis confirmed that the study was adequately powered (power = 0.82). Results: CNN+LSTM Model Performance: Achieved 92.4% classification accuracy, with false alarms reduced by 37%. Alarm Response Time: Improved by 25% (2.4 s → 1.8 s, p Conclusion: By integrating AI-driven alarm classification with psychoacoustic modeling, this study provides a novel approach to optimizing OR monitor alarms. The proposed method enhances alarm recognition, reduces false alarms, and improves surgical team response times, ultimately contributing to a safer and more efficient OR environment. Future research will focus on real-world implementation and multimodal alarm systems combining auditory, visual, and haptic feedback.