A telemedicine system to detect pre-eclampsia

Williams, Gareth J.; Jones, Geoff; Doughty, K.

doi:10.1109/iembs.1997.757814

Cited by 1 publication

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10] the comparison of two Bayesian classifiers was presented to predict the hypertensive complexity. G. Williams et al [11] proposed a telemedicine system to detect the pre-eclampsia that used electronic sensor sleeve, radio telemetry and ring less interface unit directly connected to the PSTN. Data could be measured by hospital computers for monitoring the disease.…”

Section: Prior Artmentioning

confidence: 99%

“…All of the approaches are used for measuring the hypertension, pre-eclampsia or eclampsia, but the problem is that no one explained the physical condition related to eclampsia such experiencing seizure episodes. G. Williams et al [11] presented sensor sleeve but the patient has to wear the sleeve for monitoring purpose. During the pregnancy, if the patient has hypertension and not treated on time, then eclampsia might be experienced with severe effects and consequently it becomes difficult for a woman to wear any device during this critical condition.…”

Section: Cost Effectivementioning

confidence: 99%

See 1 more Smart Citation

An efficient monitoring of eclamptic seizures in wireless sensors networks

Haider

Ren

Fan

et al. 2019

Computers & Electrical Engineering

View full text Add to dashboard Cite

This paper presents the application of wireless sensing at C-band operating at 4.8 GHz technology (a potential Chinese 5G band). A wireless transceiver is used in the indoor environment to monitor different body motions of a woman experiencing an eclamptic seizure. The body movement shows unique wireless data which carries the wireless channel information. The results indicate that using higher features increases the accuracy from 3% to 4% for classifying data from different body motions. All of the four classifiers are compared by using six performance metrics such as accuracy, recall, precession, specificity, F-measure and Kappa. The values from these metrics indicate the better performance of SVM as compared to other three classifiers, the results indicate that the eclamptic seizures are easily differentiated from other body movements by applying the aforementioned classifiers.Index Terms-C-band, Wireless channel information (WCI), Internet of Thing (IoT), support vector machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) and K-Mean.

show abstract

Section: Prior Artmentioning

confidence: 99%

Section: Cost Effectivementioning

confidence: 99%