# Evaluation of the Characteristics and Machine Learning-Based Identification Accuracy of the International Electrotechnical Commission (IEC) 60601-1-8 Medical Equipment Auditory Alarms

## TL;DR

This paper evaluates old and new medical equipment alarms and uses machine learning to improve their identification accuracy in noisy environments.

## Contribution

The study introduces a machine learning model to distinguish between old and new medical alarms, showing improved performance with newer alarm designs.

## Key findings

- New alarms have more distinct characteristics and better distinguishability even after noise degradation.
- Machine learning models achieved higher accuracy for new alarms with recall over 80% and precision above 70%.
- Identification accuracy dropped significantly for simultaneous alarms but improved when at least one was correctly classified.

## Abstract

Introduction

Auditory alarms in clinical settings signal sudden changes in a patient’s condition and failures in medical equipment. However, distinguishing between simultaneously sounding alarms, particularly when superimposed with various ambient sounds, remains challenging. This study aimed to develop a machine learning (ML) model for identifying auditory alarms issued by medical equipment.

Methods

We targeted old and new auditory alarms for medical equipment as specified in the International Electrotechnical Commission (IEC) 60601-1-8 standard. First, we evaluated the characteristics of both normal and degraded auditory alarms using cosine similarity among old and new alarms. Next, we evaluated the accuracy of ML-based identification of deteriorated alarm sound sources in both the old and new alarm groups.

Results

The cosine similarity among old alarms was over 0.99, while new alarms ranged from 0.886 to 0.985, and exhibited more distinct characteristics. When noise was superimposed, the similarity among old alarms increased further, making differentiation more difficult. In contrast, for most new alarms, cosine similarity values exceeded 0.99 but retained slight acoustic differences even after noise-induced degradation, demonstrating improved distinguishability. The accuracy for identifying a single degraded alarm sound was 71.9% for the support vector machine. The models exhibited a high number of misclassifications when identifying the old alarms. Conversely, the models achieved higher accuracy when classifying new alarms, with recall exceeding 80%, precision above 70%, and F-measure greater than 80% for all new alarms. The identification accuracies for two simultaneous alarms were under 20% and approximately 50% for old and new alarms, respectively. The accuracy declined when estimating two simultaneous new alarms; however, when at least one of the two alarms was correctly classified, the accuracy exceeded 90%.

Conclusions

This study evaluated the characteristics of old and new auditory alarms issued by medical equipment as specified in IEC 60601-1-8 and constructed ML models for identifying the type of alarms.