Biometric Statistical Study of One-Lead ECG Features and Body Mass Index (BMI)

Abstract

We have studied the electrocardiogram (ECG) as a potential biometric for human identity verification. This research investigates the relationship between ECG biometric features and body mass index (BMI) using correlation analysis and linear regression methods. Using our ECG database of 168 normal healthy people (113 females and 55 males), we studied normalized features extracted from a one-lead, resting, palm ECG. The results showed that normalized ECG biometric features explain 25.3% of the variability of the BMI. ECG features of males better correlate with the BMI model than those of females. Furthermore, we calculated correlation coefficients and R-square changes to analyze the correlations between extracted features and the BMI and to indicate the most significant feature as a predictor of BMI among all ECG biometric features.