Impact of Ambulation
In previous chapters we have discussed methods for detecting and recognizing different body movement activities (BMAs) from the analysis of the ECG signal acquired under ambulation. The ECG signals have been analyzed to show that the motion artifacts induced by various BMAs can actually be classified into different types which allows recognition of BMA from the ECG itself. The methods discussed so far deal with the commonplace BMAs performed at a normal pace. The constant normal pace of activity allowed us to exclude more complicated situations arising out of variations in the pace levels of the same BMA. However, in real life, different pace levels of any commonplace BMAs are usually described as slow, normal and fast. This kind of variations in the body movement kinematics may also have some impact on the generation of the motion artifacts in the ECG signal. In this chapter, our purpose is to determine the impact of body movement kinematics on the extent of ECG motion artifact by defining a notion called impact signal. The impact signal is derived from the ambulatory ECG signal itself. Two approaches have been adopted in this chapter to validate the basic hypothesis that the impact signal does provide a good measure of the pace of ambulation. One of them involves measuring local acceleration using motion sensors at appropriate body positions, in conjunction with the ECG, while performing routine activities at different pace levels. The other method consists of ECG acquisition during treadmill testing at controlled speeds for fixed durations. Ambulatory ECG signals and the required data about the pace of the activity have been acquired from healthy subjects as well as patients with suspected cardio-vascular disorders. In case of patients, the treadmill tests were carried out under the supervision of a cardiologist. We demonstrate that the impact signal shows a proportional increase with the increasing activity levels. The measured accelerations obtained are also found to be well correlated with the impact signal. The impact analysis thus indicates the suitability of the proposed method for quantification of body movement kinematics from the ECG signal itself, even in the absence of any accelerometer sensors. Such a quantification would also help in automated documentation of patient activity levels, which could aid in better interpretation of ambulatory ECG.
KeywordsMotion Artifact Motion Sensor Automate Documentation Treadmill Speed Instantaneous Acceleration
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