JMIR Publications

ABSTRACT

Background:

Although an increasing number of bedside medical devices are equipped with wireless connections for reliable notifications, many non-networked devices remain effective at detecting abnormal patient conditions and alerting medical staff through auditory alarms. 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 higher. 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 using 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:

The proposed Convolutional Recurrent Neural Network model was evaluated using a simulation dataset of seven alarm sounds mixed with hospital ward noise. At a signal-to-noise ratio of 0 dB, the best-performing model (CNN3+BiGRU) achieved an event-based F-score of 0.967, with a precision of 0.944 and a recall of 0.991. When the venous foot pump class was excluded, the class-wise recall of 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.