A Robust Framework for Automated Screening of Diabetic Patient Using ECG Signals

Diabetes Mellitus (DM) or diabetes is an incurable, chronic, and genetic link health problem that occurs due to the higher glucose level in the blood. Continuous monitoring and automatic screening of diabetic patients will significantly improve the quality of the medical management system. DM induce...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE sensors journal Ročník 22; číslo 24; s. 1
Hlavní autoři: Gupta, Kapil, Bajaj, Varun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
classification
Data base management systems
Decision trees
Decomposition
Diabetes
Diabetes mellitus
Electrocardiography
Entropy (Information theory)
ITD
Kernel functions
Machine learning
ISSN:1530-437X, 1558-1748
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Diabetes Mellitus (DM) or diabetes is an incurable, chronic, and genetic link health problem that occurs due to the higher glucose level in the blood. Continuous monitoring and automatic screening of diabetic patients will significantly improve the quality of the medical management system. DM induces cardiovascular autonomic neuropathy, which alters the morphology of electrocardiogram (ECG) signals. Hence, in this experiment, a new single-lead ECG signal database of 86 subjects (35 diabetic and 51 normal) is recorded. For the automatic screening of DM, an intrinsic time-scale decomposition (ITD) and machine learning-based framework is developed. In the first stage, de-noised recorded signals are segmented into fragments of 5-seconds and decomposed into rotational components using the ITD algorithm. In the second stage, four features namely, Hjorth complexity, Shannon entropy, log energy entropy, and log energy are extracted from the ITD components. In the third stage, the Kruskal-Wallis test is applied to select the most distinguishable features and fed to a decision tree classifier with three different kernel functions for the automatic detection of diabetic patients. The fine tree kernel function provides the highest classification accuracy of 86.9%. The proposed framework is developed using a 10-fold validation strategy. The developed framework is patient-centered suitable for screening in resource-limited environments and ready to be tested with more databases.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3219554