A deep learning classifier using convolutional and recurrent neural networks accurately detected medical device alarm sounds in noisy environments, achieving a recall of 0.976 and an F score of 0.945.
A deep learning classifier using convolutional and recurrent neural networks can accurately detect and classify medical device alarm sounds in noisy hospital environments.
Effect estimate: F score 0.945
BACKGROUND Although an increasing number of bedside medical devices have wireless connections, providing feasible and reliable notification, many devices without any connections perform very well in detecting abnormal status and alerting medical staff with various sounds. Staff members, however, can miss these notifications, especially when in distant areas or other private rooms. In contrast, the signal-to-noise ratio (SNR) of alarm systems for medical devices in the neonatal intensive care unit is 0 dB or more. A feasible system for automatic sound identification with high accuracy is needed to prevent alarm sounds from being missed by the staff. OBJECTIVE The purpose of this study was to design a method for classifying multiple alarm sounds collected with a monaural microphone in a noisy environment. METHODS Features of seven alarm sounds were extracted with a Mel filter bank and incorporated into a classifier using convolutional and recurrent neural networks. To estimate its clinical usefulness, the classifier was evaluated with mixtures of up to seven alarm sounds and hospital ward noise. RESULTS At an SNR of 0 dB, the recall performance of the classifier was 0.976 with an F score of 0.945. When foot pump was excluded, the class-wise recall the classifier ranged from 0.990 to 1.000. CONCLUSIONS The proposed classifier was found to be highly accurate in detecting alarm sounds. Although the performance of the proposed classifier in a clinical environment can be improved, the classifier could be incorporated into an alarm sound detection system. The classifier, combined with network connectivity, could improve the notification of abnormal status detected by unconnected medical devices.
Kishimoto et al. (2025) studied Medical device alarm sounds. Deep Alarm Sound Detection classifier was evaluated on Recall performance and F score at an SNR of 0 dB (F score 0.945). A deep learning classifier using convolutional and recurrent neural networks accurately detected medical device alarm sounds in noisy environments, achieving a recall of 0.976 and an F score of 0.945.