Identification of Diabetic Patients Using the Nonlinear Analysis of Short-Term Heart Rate Time Series

Кривенко, С. А.; Krivenko, S. S.; Pulavskyi, A. A.

Публікація:
Identification of Diabetic Patients Using the Nonlinear Analysis of Short-Term Heart Rate Time Series

dc.contributor.author	Кривенко, С. А.
dc.contributor.author	Krivenko, S. S.
dc.contributor.author	Pulavskyi, A. A.
dc.date.accessioned	2020-08-14T09:38:36Z
dc.date.available	2020-08-14T09:38:36Z
dc.date.issued	2018
dc.description.abstract	The non-invasive method for identifying the volunteers suffering from the type 2 diabetes mellitus (T2DM) is suggested. The method is based on the symbolic analysis of short series (~300 points) of RR-intervals of a single-lead electrocardiogram. To obtain the initial symbol sequences, 4 different methods of formation of symbols from the time series were used. Using the SVM classifier with a linear kernel, a selection of significant symbols was made. The most significant symbols became the input parameters for the SVM model with RBF kernel. The model has shown high efficiency: the sensitivity on the test sets was 70-82%, and the specificity was 73-77%. The proposed method uses the posterior probability, which has been accompanied by a class label for each new sample, being a criterion of the results reliability. For the obtained model, the threshold value of the posterior probability was 91%. It has been shown that the use of the posterior probability does not impair or improve the quality of the forecast. While using the posterior probability, the sensitivity of the model can increase up to 88% and specificity can increase up to 90%, being objective for up to 50% of all predicted values.	uk_UA
dc.identifier.citation	S. S. Krivenko, A. A. Pulavskyi and S. A. Krivenko, "Identification of Diabetic Patients Using the Nonlinear Analysis of Short-Term Heart Rate Time Series," 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), Kiev, 2018, pp. 249-254, doi: 10.1109/ELNANO.2018.8477587.	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/12690
dc.language.iso	en_US	uk_UA
dc.publisher	IEEE	uk_UA
dc.relation.ispartofseries	;doi: 10.1109/ELNANO.2018.8477587.
dc.subject	Predictive models	uk_UA
dc.subject	Reliability	uk_UA
dc.subject	Time series analysis	uk_UA
dc.subject	Sensitivity	uk_UA
dc.subject	Kernel	uk_UA
dc.subject	Support vector machines	uk_UA
dc.subject	Diabetes	uk_UA
dc.title	Identification of Diabetic Patients Using the Nonlinear Analysis of Short-Term Heart Rate Time Series	uk_UA
dc.type	Conference proceedings	uk_UA
dspace.entity.type	Publication