dSVRI: A PPG-based Novel Feature for Early Diagnosis of Type-II Diabetes Mellitus

• Published in: IEEE sensors letters Vol. 6; no. 9; pp. 1 - 4
• Main Authors: Gupta, Shresth, Singh, Anurag, Sharma, Abhishek, Tripathy, Rajesh Kumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blood pressure; Diabetes; Diabetes mellitus; Diabetic neuropathy; Diagnosis; dSVRI; Feature extraction; grid search random forest; Heart rate; Hyperglycemia; Indexes; Insulin; Metabolic disorders; Pathology; PPG; sensor signal processing; Time domain analysis
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2022.3203609

Diabetes is a group of metabolic disorders characterized by hyperglycemia caused by a deficiency in insulin production, insulin action, or both. Type-II diabetes mellitus (DM-2) complications include retinopathy, cardiovascular disorder and diabetic neuropathy. The existing works for the diagnosis of DM-2 using photoplethysmogram (PPG) utilize several time-domain features and demographic parameters of the individuals. However, the current features do not indicate a clinical correlation between DM-2 and its functional influence on cardiovascular regulation. This work proposes a novel index, called dSVRI, as a feature based on systemic vascular resistance pathology to discriminate between a healthy and diabetic subject. The discrimination ability and the diagnostic performance of the proposed dSVRI were compared with the existing time-domain PPG features and its higher derivatives. In experiments with a publicly available data-set (having 219 subjects including healthy and DM-2 individuals), an accuracy of 98.52% is obtained using grid search random forest, which is significantly higher than the existing methods.