Electrocardiogram (ECG) signals are electrical signals that originate from the heart and are important in diagnosing and monitoring heart conditions. With the advent of e-health and u-health programs, etc., in recent times, there is an interest in technology for measuring ECG signals in a mobile environment.
A major problem for measuring ECG signals in a mobile environment is low signal-to-noise ratio (hereinafter, “SNR”). In a typical hospital, ECG signals are measured for a stable posture in a stable environment using high-performance equipment, but in a mobile environment, the ECG measurement may be performed for a subject that is not always in a stable posture, and the equipment used may be a compact device that provides a relatively low performance.
For ECG signals obtained in such an environment, it may not be possible to sufficiently remove noise using conventional, typical noise removal methods.
That is, conventional ECG noise removal methods can be divided mainly into methods that use general high/band/low pass filters and methods that use wavelets. These conventional methods are effective when applied to signals having a somewhat high SNR, but if they are applied to ECG signals having a low SNR and noise is removed excessively, the shapes of waveforms such as the QRS-complex, P-wave, T-wave, etc., essential in diagnosing a human body, may be greatly altered.