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Baseline Drift

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Brian Hoit – One of the best experts on this subject based on the ideXlab platform.

  • Novel long-term implantable blood pressure monitoring system with reduced Baseline Drift
    Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings, 2006
    Co-Authors: Peng Cong, Brian Hoit


    A novel long-term less-invasive blood pressure monitoring system with fluid-filled cuff is proposed for advanced biological research. The system employs an instrumented elastic cuff attached with a rigid isolation ring on the outside wall of the cuff. The cuff is wrapped around a blood vessel for real-time blood pressure monitoring. The elastic cuff is made of bio-compatible soft silicone material and is filled with bio-compatible insulating silicone oil with an immersed MEMS pressure sensor. This technique avoids vessel penetration and substantially minimizes vessel restriction due to the soft cuff elasticity, thus attractive for long-term monitoring. A rigid isolation ring is used to isolate the cuff from environmental variations to suppress Baseline Drift in the measured waveform inside the monitoring cuff. The prototype monitoring cuff is wrapped around the right carotid artery of a laboratory rat to measure real-time blood pressure waveform. The measured in vivo blood waveform is compared with a reference waveform recorded simultaneously by using a commercial catheter-tip transducer inserted into the left carotid artery, showing matched waveforms with a scaling factor about 0.03 and a Baseline Drift of 0.6 mm Hg. The measured Baseline Drift is three times smaller compared to using a cuff without a rigid isolation ring.

P Keall – One of the best experts on this subject based on the ideXlab platform.

  • real time profiling of respiratory motion Baseline Drift frequency variation and fundamental pattern change
    Physics in Medicine and Biology, 2009
    Co-Authors: D Ruan, Jeffrey A Fessler, James M Balter, P Keall


    To precisely ablate tumor in radiation therapy, it is important to locate the tumor position in real time during treatment. However, respiration-induced tumor motions are difficult to track. They are semi-periodic and exhibit variations in Baseline, frequency and fundamental pattern (oscillatory amplitude and shape). In this study, we try to decompose the above-mentioned components from discrete observations in real time. Baseline Drift, frequency (equivalently phase) variation and fundamental pattern change characterize different aspects of respiratory motion and have distinctive clinical indications. Furthermore, smoothness is a valid assumption for each one of these components in their own spaces, and facilitates effective extrapolation for the purpose of estimation and prediction. We call this process ‘profiling’ to reflect the integration of information extraction, decomposition, processing and recovery. The proposed method has three major ingredients: (1) real-time Baseline and phase estimation based on elliptical shape tracking in augmented state space and Poincare sectioning principle; (2) estimation of the fundamental pattern by unwarping the observation with phase estimate from the previous step; (3) filtering of individual components and assembly in the original temporal-displacement signal space. We tested the proposed method with both simulated and clinical data. For the purpose of prediction, the results are comparable to what one would expect from a human operator. The proposed approach is fully unsupervised and data driven, making it ideal for applications requiring economy, efficiency and flexibility.

Behzad Mozaffary – One of the best experts on this subject based on the ideXlab platform.

  • a wavelet packets approach to electrocardiograph Baseline Drift cancellation
    International Journal of Biomedical Imaging, 2006
    Co-Authors: Mohammad Ali Tinati, Behzad Mozaffary


    Baseline wander elimination is considered a classical problem. In electrocardiography (ECG) signals, Baseline Drift can influence the accurate diagnosis of heart disease such as ischemia and arrhythmia. We present a wavelet-transform- (WT-) based search algorithm using the energy of the signal in different scales to isolate Baseline wander from the ECG signal. The algorithm computes wavelet packet coefficients and then in each scale the energy of the signal is calculated. Comparison is made and the branch of the wavelet binary tree corresponding to higher energy wavelet spaces is chosen. This algorithm is tested using the data record from MIT/BIH database and excellent results are obtained.