A Hybrid Convolutional Recurrent Neural Model for Noise Detection and Automated Calibration of Medical Devices via Molecular Vibration Analysis

Abstract Accurate noise detection and calibration are essential for maintaining the measurement reliability of medical devices. This study proposes a hybrid convolutional recurrent neural network (CRNN) framework for noise detection and automated calibration of ventilators and electrocardiogram (ECG) machines using molecular vibration analysis. A molecular vibration model was developed to characterize mechanical vibration, electromagnetic interference, and environmental conduction noise, achieving an average noise classification accuracy of 96.2%. The proposed CRNN with hybrid attention maintained 91.5% accuracy under low signal-to-noise conditions. A deep deterministic policy gradient-based calibration strategy was subsequently employed to optimize calibration parameters automatically, reducing calibration time by 72.5% and improving calibration accuracy by 78.5% compared with conventional methods. After calibration, the average relative errors were 1.9% for ventilator tidal-volume measurements and 0.92% for QRS-complex amplitude measurements in ECG devices. Cross-device validation achieved 94.3% accuracy across different equipment brands. The results demonstrate that integrating molecular vibration analysis, hybrid deep learning, and automated calibration provides an effective approach for reliable noise detection and precision calibration of medical devices.