Physiological measurement
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Physiological measurement · May 2008
Robust electrocardiogram (ECG) beat classification using discrete wavelet transform.
This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG). Features extracted from the QRS complex of the ECG using a wavelet transform along with the instantaneous RR-interval are used for beat classification. The wavelet transform utilized for feature extraction in this paper can also be employed for QRS delineation, leading to reduction in overall system complexity as no separate feature extraction stage would be required in the practical implementation of the system. ⋯ Another main advantage of this method is its robustness to noise, which is illustrated in this paper through experimental results. Furthermore, principal component analysis (PCA) has been used for feature reduction, which reduces the number of features from 11 to 6 while retaining the high beat classification accuracy. Due to reduction in computational complexity (using six features, the time required is approximately 4 ms per beat), a simple classifier and noise robustness (at 10 dB signal-to-noise ratio, accuracy is 95%), this method offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.
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Physiological measurement · May 2008
Effect of confounding factors on blood pressure estimation using pulse arrival time.
Two confounding factors were selected and analyzed in blood pressure estimation using pulse arrival time (PAT) for each individual. The heart rate was used as the confounding factor for the cardiac cycle, and the duration from the maximum derivative point to the dicrotic peak (TDB) in the photoplethysmogram was used as another confounding factor representing arterial stiffness. ⋯ The correlation between estimated and measured blood pressure decreased a little, but the validity was still maintained (r congruent with 0.8). This shows the value of the method in non-intrusive blood pressure estimation for individual patients and may be useful for various applications.
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Physiological measurement · May 2008
Model-based Bayesian filtering of cardiac contaminants from biomedical recordings.
Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.