Name
Abstract | ||
|---|---|---|
Every day, a substantial number of people need to be treated in emergencies and these situations imply a short timeline. Especially concerning heart abnormalities, the time factor is very important. Therefore, we propose a full-stack system for faster and cheaper ECG taking aimed at paramedics, to enhance Emergency Medical Service (EMS) response time. To stick with the golden hour rule, and reduce the cost of the current devices, the system is capable of enabling the detection and annotation of anomalies during ECG acquisition. Our system combines Machine Learning and traditional Signal Processing techniques to analyze ECG tracks to use it in a glove-like wearable. Finally, a graphical interface offers a dynamic view of the whole procedure. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9175906 | 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
Keywords | DocType | Volume |
Electrocardiography,Emergency Medical Services,Machine Learning,Signal Processing, Computer-Assisted,Time Factors | Conference | 2020 |
ISSN | ISBN | Citations |
2375-7477 | 978-1-7281-1991-5 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Di Gennaro | 1 | 0 | 0.34 |
| Luigi Fusco | 2 | 0 | 0.34 |
| Ian Di Dio Lavore | 3 | 0 | 0.34 |
| Eleonora D'Arnese | 4 | 0 | 0.34 |
| Marco D. Santambrogio | 5 | 771 | 91.15 |