Detection of Polyphonic Alarm Sounds From Medical Devices Using Frequency-Enhanced Deep Learning: Simulation Study.

ABSTRACT:

Background

Although an increasing number of bedside medical devices are equipped with wireless connections for reliable notifications, many nonnetworked 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 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

SUBMITTER: Kishimoto K 

PROVIDER: S-EPMC12611226 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature