Adaptive Filtering Improved Apnea Detection Performance Using Tracheal Sounds in Noisy Environment: A Simulation Study

Abstract EN

Objective.

Tracheal sounds were used to detect apnea on various occasions.

However, ambient noises can contaminate tracheal sounds which result in poor performance of apnea detection.

The objective of this paper was to apply the adaptive filtering (AF) algorithm to improve the quality of tracheal sounds and examine the accuracy of the apnea detection algorithm using tracheal sounds after AF.

Method.

Tracheal sounds were acquired using a primary microphone encased in a plastic bell, and the ambient noises were collected using a reference microphone resting outside the plastic bell in quiet and noisy environments, respectively.

Simultaneously, the flow pressure signals and thoracic and abdominal movement were obtained as the standard signals to determine apnea events.

Then, the normalized least mean square (NLMS) AF algorithm was applied to the tracheal sounds mixed with noises.

Finally, the algorithm of apnea detection was used to the tracheal sounds with AF and the tracheal sounds without AF.

Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s kappa coefficient of apnea detection were calculated.

Results.

Forty-six healthy subjects, aged 18-35 years and with BMI<21.4, were included in the study.

The apnea detection performance using tracheal sounds was as follows: in the quiet environment, the tracheal sounds without AF detected apnea with 97.2% sensitivity, 99.9% specificity, 99.8% PPV, 99.4% NPV, 99.5% accuracy, and 0.982 kappa coefficient.

The tracheal sounds with AF detected apnea with 98.2% sensitivity, 99.9% specificity, 99.4% PPV, 99.6% NPV, 99.6% accuracy, and 0.985 kappa coefficient.

While in the noisy environment, the tracheal sounds without AF detected apnea with 81.1% sensitivity, 96.9% specificity, 85.1% PPV, 96% NPV, 94.2% accuracy, and 0.795 kappa coefficient and the tracheal sounds with AF detected apnea with 91.5% sensitivity, 97.4% specificity, 88.4% PPV, 98.2% NPV, 96.4% accuracy, and 0.877 kappa coefficient.

Conclusion.

The performance of apnea detection using tracheal sounds with the NLMS AF algorithm in the noisy environment proved to be accurate and reliable.

The AF technology could be applied to the respiratory monitoring using tracheal sounds.