Type 2 Diabetes Screening Test by Means of a Pulse Oximeter

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 2; pp. 341 - 351
• Main Authors: Moreno, Enrique Monte, Lujan, Maria Jose Anyo, Rusinol, Montse Torrres, Fernandez, Paqui Juarez, Manrique, Pilar Nunez, Trivino, Cristina Aragon, Miquel, Magda Pedrosa, Rodriguez, Marife Alvarez, Burguillos, M. Jose Gonzalez
• Format: Journal Article Publication
• Language: English
• Published: United States IEEE 01.02.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aged; Algorithms; Aparells mèdics; Biomedical engineering; Blood; Boosting; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - diagnosis; Diagnosis, Computer-Assisted - methods; Discriminant analysis; Electrocardiography; Enginyeria biomèdica; Feature extraction; Heart Rate - physiology; Heart rate variability; Hemoglobin; Humans; Learning algorithms; Machine learning; Middle Aged; Noninvasive treatment; Oximetry - methods; Oxygen - blood; Photoplethysmography; Plasmas; Pulse oximetry; ROC Curve; Screening; Signal processing; Signal Processing, Computer-Assisted; Statistical learning; Sugar; Type 2 Diabetes; Àrees temàtiques de la UPC
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2016.2554661

In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts: the first analyzes the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e., whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of 1157 subjects (two samples per subject) collected from five community health centers. The mean receiver operating characteristic area found was 69.4% (median value 71.9% and range [75.4-61.1%]), with a specificity = 64% for a threshold that gave a sensitivity = 65%. We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than 5 min.