Investigation on cardiovascular risk prediction using genetic information.

Cardiovascular disease (CVD) has become the primary killer worldwide and is expected to cause more deaths in the future. Prediction and prevention of CVD have therefore become important social problems. Many groups have developed prediction models for asymptomatic CVD by classifying its risk based on established risk factors (e.

Comparison of median filter and discrete dyadic wavelet transform for noise cancellation in electrocardiogram.

Development of noise cancellation algorithm is essential to facilitate accurate detection of electrocardiogram (ECG) in mobile health and wearable medical devices. In this study, we captured ECG from 20 subjects when they were at rest and during routine activities. The motion artifact in ECG was filtered using two non-linear filters: median filter and discrete dyadic wavelet transform.