# Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/machine-learning-techniques-for-the-detection-of-shockable-rhythms-in-automated/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Carlos Figuera,Unai Irusta,Eduardo Morgado,Elisabete Aramendi,Unai Ayala,Lars Wik,Jo Kramer‐Johansen,Trygve Eftestøl,Felipe Alonso‐Atienza |
| Citations | 75 |
| DOI | 10.1371/journal.pone.0159654 |
| Fields | Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0159654&type=printable |
| OpenAlex ID | https://openalex.org/W2492557229 |
| PMID | 27441719 |
| Type | article |
| Year | 2016 |

## Paper authors

- [Unai Irusta](https://scholariq.org/researchers/unai-irusta/)

## Paper journal

- [PLoS ONE](https://scholariq.org/journals/plos-one/)

## Paper primary topic

- [Cardiac Arrest and Resuscitation](https://scholariq.org/topics/cardiac-arrest-and-resuscitation/)

## Paper topics

- [Cardiac Arrest and Resuscitation](https://scholariq.org/topics/cardiac-arrest-and-resuscitation/)
- [ECG Monitoring and Analysis](https://scholariq.org/topics/ecg-monitoring-and-analysis/)
- [Healthcare Technology and Patient Monitoring](https://scholariq.org/topics/healthcare-technology-and-patient-monitoring/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.