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논문 기본 정보

자료유형
학술저널
저자정보
R. Shashikant (College of Engineering) Uttam Chaskar (College of Engineering) Leena Phadke (Kashibai Navale Medical College and General l Hospital) Chetankumar Patil (College of Engineering Pune)
저널정보
대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.11 No.3
발행연도
2021.8
수록면
273 - 286 (14page)
DOI
https://doi.org/10.1007/s13534-021-00196-7

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The main objective of the study was to develop a low-cost, non-invasive diagnostic model for the early prediction of T2DMrisk and validation of this model on patients. The model was designed based on the machine learning classifi cation techniqueusing non-linear Heart rate variability (HRV) features. The electrocardiogram of the healthy subjects (n = 35) and T2DMsubjects (n = 100) were recorded in the supine position for 15 min, and HRV features were extracted. The signifi cant nonlinearHRV features were identifi ed through statistical analysis. It was found that Poincare plot features (SD1 and SD2) candiff erentiate the T2DM subject data from healthy subject data. Several machine learning classifi ers, such as Linear DiscriminantAnalysis (LDA), Quadratic Discriminant Analysis, Naive Bayes, and Gaussian Process Classifi er (GPC), have classifi edthe data based on the cross-validation approach. A GP classifi er was implemented using three kernels, namely radial basis,linear, and polynomial kernel, considering the ability to handle the non-linear data. The classifi er performance was evaluatedand compared using performance metrics such as accuracy(AC), sensitivity(SN), specifi city(SP), precision(PR), F1 score,and area under the receiver operating characteristic curve(AUC). Initially, all non-linear HRV features were selected forclassifi cation, but the specifi city of the model was the limitation. Thus, only two Poincare plot features were used to designthe diagnostic model. Our diagnostic model shows the performance using GPC based linear kernel as AC of 92.59%, SNof 96.07%, SP of 81.81%, PR of 94.23%, F1 score of 0.95, and AUC of 0.89, which are more extensive compared to otherclassifi cation models. Further, the diagnostic model was deployed on the hardware module. Its performance on unknown/testdata was validated on 65 subjects (healthy n = 15 and T2DM n = 50). Considering the desirable performance of the diagnosticmodel, it can be used as an initial screening test tool for a healthcare practitioner to predict T2DM risk.
#Type 2 diabetes mellitus #Diagnostic model #Heart rate variability #Electrocardiogram #Poincare plot #Detrended fl uctuation analysis #Gaussian process classifi er

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참고문헌 (40)

참고문헌 신청
Acharya UR / 2006 / Heart rate variability : a review / Med Biol Eng Compu 44(12):1031~1051 google schola Williams SM / 2019 / Cardiac autonomic neuropathy in obesity, the metabolic syndrome, and prediabetes : a narrative review / Diabetes Therapy 1:1~27 google schola Vincenza Spallone / 2019 / Update on the Impact, Diagnosis and Management of Cardiovascular Autonomic Neuropathy in Diabetes: What Is Defined, What Is New, and What Is Unmet / Diabetes and Metabolism Journal 43(1):3~30 dbpia Sardu C / 2019 / Diabetes mellitus and its cardiovascular complications: new insights into an old disease 1:1~2 google schola Rydén L / 2013 / ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD : the Task Force on diabetes, prediabetes, and cardiovascular diseases of the European Society of Cardiology(ESC)and developed in collaboration with the European Association for the Study of Diabetes(EASD) / Eur Heart J 34(39):3035~3087 google schola

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