Name
Abstract | ||
|---|---|---|
The advent of remote and wearable medical sensing has created a dire need for efficient medical time series databases. Wearable medical sensing devices provide continuous patient monitoring by various types of sensors and have the potential to create massive amounts of data. Therefore, time series databases must utilize highly optimized indexes in order to efficiently search and analyze stored data. This paper presents a highly efficient technique for indexing medical time series signals using Locality Sensitive Hashing (LSH). Unlike previous work, only salient (or interesting) segments are inserted into the index. This technique reduces search times by up to 95% while yielding near identical search results. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EMBC.2012.6347137 | EMBC |
Keywords | Field | DocType |
medical information systems,high performance biomedical time series index,locality sensitive hashing,patient monitoring,remote medical sensing,database indexing,salient segmentation,time series,wearable medical sensing | Locality-sensitive hashing,Data mining,Computer science,Remote patient monitoring,Segmentation,Wearable computer,Search engine indexing,Database index,Salient | Conference |
Volume | ISSN | ISBN |
2012 | 1557-170X | 978-1-4577-1787-1 |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Woodbridge | 1 | 69 | 8.50 |
| Bobak Mortazavi | 2 | 45 | 8.38 |
| Alex Bui | 3 | 318 | 48.20 |
| Majid Sarrafzadeh | 4 | 3103 | 317.63 |