Name
Abstract | ||
|---|---|---|
Prior work has shown the classification of voiding dysfunctions from uroflowmeter data using machine learning. We present the use of smartwatch audio, collected through the UroSound platform, in order to automatically classify voiding signals as normal or abnormal, using classical machine learning techniques. We train several classification models using classical machine learning and report a maximal test accuracy of 86.16% using an ensemble method classifier. Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous audio capture devices such as smartphones and smartwatches. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9871611 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Girish Narayanswamy | 1 | 0 | 0.34 |
| Laura Arjona | 2 | 0 | 0.34 |
| Luis E Diez | 3 | 0 | 0.34 |
| Alfonso Bahillo | 4 | 10 | 5.12 |
| Shwetak Patel | 5 | 18 | 2.12 |