Nonintrusive Arrhythmia Detection from Wrist Pulse Using NTSC Color Model in Eulerian Video Magnification

Abstract

Arrhythmia is a cardiovascular condition characterized by abnormal heart rhythms, such as tachycardia and bradycardia, which may lead to serious health complications if not detected early. This study proposes a non-invasive approach for screening tachycardia by extracting pulse signals from wrist video recordings using Eulerian Video Magnification (EVM) combined with the NTSC color space model. Subtle variations in skin color caused by blood flow, which are typically imperceptible to the human eye, are amplified using the EVM technique to enhance pulse-related motion signals. The NTSC color model is employed to separate luminance and chrominance components (YIQ), allowing more effective identification of pulse-induced color variations in the wrist region. The recorded wrist videos are processed through several stages, including spatial decomposition, temporal filtering, motion magnification, and pixel intensity extraction from the region of interest to obtain a temporal pulse signal. Peak detection is then applied to estimate heart rate in beats per minute (BPM). The performance of the proposed method is evaluated by comparing the estimated BPM values with reference measurements obtained from a Xiaomi Mi Band 2 wearable device. Experimental results based on 20 wrist video recordings demonstrate that the proposed method achieves approximately 96% agreement between the estimated BPM values and the reference measurements. Quantitative evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation analysis further confirms the consistency of the proposed approach. These results indicate that the integration of Eulerian Video Magnification with the NTSC color model has potential as a low-cost and non-contact method for preliminary tachycardia screening and remote cardiovascular monitoring.