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The Electrocardiogram(ECG) signal is one of the bio-signals to check body status. Traditionally,
the ECG signal was checked in the hospital. In these days, as the number of people who is interesting
with periodic their health check increase, the requirement of self-diagnosis system development is being
increased as well. Ubiquitous concept is one of the solutions of the self-diagnosis system. Zigbee wireless
sensor network concept is a suitable technology to satisfy the ubiquitous concept. In measuring ECG
signal, there are several kinds of methods in attaching electrode on the body called as Lead I, II, III, etc.
In addition, several noise components occurred by different measurement situation such as
experimenter's respiration, sensor's contact point movement, and the wire movement attached on sensor
are included in pure ECG signal. Therefore, this paper is based on the two kinds of development concept.
The first is the Zibee wireless communication technology, which can provide convenience and
simpleness, and the second is motion artifact remove algorithm, which can detect clear ECG signal from
measurement subject. The motion artifact created by measurement subject's movement or even
respiration action influences to distort ECG signal, and the frequency distribution of the noises is around
from 0.2Hz to even 30Hz. The frequencies are duplicated in actual ECG signal frequency, so it is
impossible to remove the artifact without any distortion of ECG signal just by using low-pass filter or
high-pass filter. The suggested algorithm in this paper has two kinds of main parts to extract clear ECG
signal from measured original signal through an electrode. The first part is to extract motion noise signal
from measured signal, and the second part is to extract clear ECG by using extracted motion noise signal
and measured original signal. The paper suggests several techniques in order to extract motion noise
signal such as predictability estimation theory, low pass filter, a filter including a moving weighted factor,
peak to peak detection, and interpolation techniques. In addition, this paper introduces an adaptive filter
in order to extract clear ECG signal by using extracted baseline noise signal and measured signal from
sensor.
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