Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.14621 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917960831991808 |
|---|---|
| author | Farayola, Grace Funmilayo Akintola, Akinyemi Sadeeq Fagbohun, Oluwole Oforgu, Chukwuka Michael Kayode, Bisola Faith Chimezie, Christian Kadri, Temitope Oludotun, Abiola Ogbeide, Nelson Michael, Mgbame Ifaturoti, Adeseye Oloyede, Toyese |
| author_facet | Farayola, Grace Funmilayo Akintola, Akinyemi Sadeeq Fagbohun, Oluwole Oforgu, Chukwuka Michael Kayode, Bisola Faith Chimezie, Christian Kadri, Temitope Oludotun, Abiola Ogbeide, Nelson Michael, Mgbame Ifaturoti, Adeseye Oloyede, Toyese |
| contents | False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patient safety. Ventricular tachycardia (VT) alarms are particularly difficult to detect accurately due to their complex nature. This paper presents a machine learning approach to reduce false VT alarms using the VTaC dataset, a benchmark dataset of annotated VT alarms from ICU monitors. We extract time-domain and frequency-domain features from waveform data, preprocess the data, and train deep learning models to classify true and false VT alarms. Our results demonstrate high performance, with ROC-AUC scores exceeding 0.96 across various training configurations. This work highlights the potential of machine learning to improve the accuracy of VT alarm detection in clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14621 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach Farayola, Grace Funmilayo Akintola, Akinyemi Sadeeq Fagbohun, Oluwole Oforgu, Chukwuka Michael Kayode, Bisola Faith Chimezie, Christian Kadri, Temitope Oludotun, Abiola Ogbeide, Nelson Michael, Mgbame Ifaturoti, Adeseye Oloyede, Toyese Machine Learning Artificial Intelligence False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patient safety. Ventricular tachycardia (VT) alarms are particularly difficult to detect accurately due to their complex nature. This paper presents a machine learning approach to reduce false VT alarms using the VTaC dataset, a benchmark dataset of annotated VT alarms from ICU monitors. We extract time-domain and frequency-domain features from waveform data, preprocess the data, and train deep learning models to classify true and false VT alarms. Our results demonstrate high performance, with ROC-AUC scores exceeding 0.96 across various training configurations. This work highlights the potential of machine learning to improve the accuracy of VT alarm detection in clinical settings. |
| title | Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.14621 |