An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes

Abstract

Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, L(mean)) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks.

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

“…The significant features can be used to derive an expression, such that for each class, the expression results in a unique range of values [49][50][51][52][53][54]. Such an index can be used to characterize the state of a physiological condition [49].…”

“…Using a single index, two or more classes can be discriminated. This concept of the integrated index is used to diagnose diabetes using heart rate signals [50], coronary artery disease (CAD) using ultrasound images [51], malignant and benign thyroid using ultrasound images [52], CAD using heart rate signals [53] and diabetes retinopathy using fundus images [54].…”

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

“…However, such thresholding methods can only be used on a single scalar value. One way to overcome that drawback is to incorporate multiple features into one index value and to present the resulting value to the threshold classifier [60]. However, the creation of such index values is based on the intuition and experience of the experimenter.…”

Formal and Model Driven Design of the Bright Light Therapy System Luxamet

“…For example, breast imaging for cancer detection relies on sophisticated image processing algorithms [60,61]. Similarly, Computer-Aided Diagnosis (CAD) systems for plaque [62][63][64][65], cardiac disease [66,67] and diabetes [68,69] relay also heavily on computerized processing. Hence, these CAD systems stand to benefit from formal and model driven biomedical systems design, because the design methodology helps us to realize systemic safety and reliability.…”