Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension—the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient’s current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems.
An efficient ECG modeling algorithm is presented in this paper. The model is based on fitting polynomial functions to real ECG. This algorithm describes a segmentation of a heartbeat and fitting an appropriate polynomial function to the segments. The model performances are evaluated in terms of PRD, preserve of ST-T segment clinical information and the execution time. When comparing this model with the existing one, the PRD improvements can be seen, especially in those signals with high morphological diversity heartbeats. Moreover, the computing time is significantly reduced. Using appropriate model features, ST-T analysis achieves an average accuracy of more than 94%. The obtained data shows that this model is applicable to other ECG processing like: heartbeat analysis, compression and filtering. Ill. 2, bibl. 9, tabl. 3 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.110.4.304
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