Online mining abnormal period patterns from multiple medical sensor data streams
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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most papers using anomaly detection usually deal with short-term and multivariate data sets to characterize entire data to find discords.
“According to our review, most of the papers using the anomaly detection approach usually deal with short term [30] and multivariate data sets [31] in order to characterize the entire the data to find discords.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
Most works used categorized and complex multivariate data sets with formal definitions and annotations by domain experts to evaluate quantitatively and test the performance of output decisions.
“the most of the works used categorized and complex multivariate data sets with formal definitions and annotations by domain expert [30,40,45].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
Blood pressure data has appeared in the literature in both continuous and discrete format.
“Blood pressure data is an example which has been appeared in the literature in both continuous and discrete format [30,75].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
From the data mining point of view, the continuity of physiological data is important because dealing with streams of sensor data has its own problems and challenges as well as analyzing sporadic measurements.
“From the data mining point of view, the continuity of physiological data is important, since dealing with streams of sensor data has its own problems and challenges as well as analyzing sporadic measurements [30].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
Most research has used several sensors to have global reasoning in health monitoring.
“Besides the works focusing on physiological data mining gathered from a single sensor, most of the researches have used several sensors [30,40,44,67] to have global reasoning in health monitoring.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
Although using several wearable sensors in health monitoring frameworks is common, few research has really performed multivariate data analysis to extract effective information through multi-sensor data.
“Although using several wearable sensors in health monitoring frameworks are common, but few researches have really performed the multivariate data analysis in order to extract effective information through multi sensor data [30].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
Some attention to exploiting multiple measurements of vital signs simultaneously has been given in the literature.
“Some attention to this issue has been given in the literature such as [30].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [30]
Cites
Charbonnier, S., & Gentil, S. (2010) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.1123 · Cited by 26 · see citing works
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Huang, G., Zhang, Y., Cao, J., Steyn, M., & Taraporewalla, K.
- Affiliations
- Victoria University (AU)
- Nanjing University of Finance and Economics (CN)
- Royal Brisbane and Women's Hospital (AU)
- Corresponding author
- Huang, Guangyan
- Venue
- World Wide Web
- Published
- 2013-02-09
- Volume
- 17
- Issue
- 4
- Pages
- 569-587
- ISSN
- 1386-145X (ISSN-L)
- 1573-1413
- OpenAlex
- W2106570511
- Cited by
- 27
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 1 · 2019: 3 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 6 · 2014: 1 · 2013: 1
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- World Wide Web (publishedVersion)
- Victoria University Research Repository (Victoria University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- medical sensor data, intensive care unit, alarm reduction, false alarm reduction, anomaly detection, pattern mining, heart rate, arterial pressure, respiratory impedance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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