Related Experiment Video
Updated: Jun 4, 2026

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
Automated diagnosis of diabetes using heart rate variability signals
Ahamed Seyd P T1, Paul K Joseph, Jeevamma Jacob
1Department of Electrical Engineering, National Institute of Technology, Calicut, Kerala, India. ahamedseyd@yahoo.com
Abstract:
An automated diagnostic system for diabetes mellitus (DM), from heart rate variability (HRV) measures, using feed forward neural network has been developed. Changes in autonomic nervous system activity caused by DM are quantified by means of time domain and frequency domain analysis of HRV. Electrocardiograms of 70 DM patients and 65 healthy volunteers were recorded. Nine time domain measures-standard deviation of all NN intervals, square root of mean of sum of squares of differences between adjacent NN interval (RMSSD), number of adjacent NN intervals differing more than 50 ms. (NN50 count), percentage of NN50 count, R-R triangular index, triangular interpolation of NN intervals (TINN), standard deviation of the mean heart rate, mean R-R interval and mean heart rate-were used as the input features to the neural network. This diagnostic system classifies DM patients and normal volunteers from morphologically identical ECGs. Diagnostic results show that the system is performing well with an accuracy of 93.08%, specificity of 96.92% and sensitivity of 89.23%.
Related Concept Videos
Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...
Dysrhythmias V: Evaluating Dysrhythmias
Assessing Blood pressure using a doppler ultrasound
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
Holter Monitor: 24-Hour Monitoring
Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis
Heart Failure IV: Classification and Diagnostic Evaluation
