| Abstrakt: |
Accidents may occur in hospitals when the medical staff fail to notice the alarm ringing at a distance or in a closed room. In many hospitals, patient monitoring devices are connected to the hospital information system through a network, but some medical devices cannot be connected because they do not produce any external output. If the staff can detect the alarm ringing in a hospital room from some distance, they can provide more efficient and proactive medical care. In this study, alarm sounds were collected using a monaural microphone, and a machine learning classifier was constructed using deep neural networks. The classifier was evaluated using a simulation dataset of polyphonic alarm sounds, superimposed with the environmental sounds of a hospital ward. Data were collected from four devices, and two training datasets were created with a logarithm spectrogram using Mel filter bank (MFB) and custom filter bank (CFB). In addition, two classifiers were developed for 16 classes based on a combination of the four devices. One classifier was trained on MFB and the other on CFB. The classifiers evaluated the simulation dataset with a signal-to-noise ratio (SNR) of 30, 20, 10, and 0 dB. The classifier trained on CFB had a micro F1 score of 72.7% and an area-under-the-ROC-curve of 0.963 at an SNR of 0 dB. This micro F1 score was 4.5 points higher than that of the score of the classifier trained on MFB. In addition, the misidentification rate of the environmental sounds (class without all devices) was 1.2%. Therefore, the classifier could not reliably distinguish between the alarm sound and environmental sounds, but the possibility as a notification system was presented. [ABSTRACT FROM AUTHOR] |
