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A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and(More)
Feed-Forward Neural Network (FFNN) has recently been utilized to estimate blood pressure (BP) from the oscillometric measurements. However, there has been no study till now that consolidated the role played by the different neural network (NN) training algorithms in affecting the BP estimates. This paper compares the estimation errors in the BP due to ten(More)
In this paper, we present a computationally efficient method for adaptive tracking of physiological parameters such as heart rate and respiratory rate from the arterial blood pressure (ABP) measurement using particle filters. A previously reported estimation and tracking method was based on approximating the nonlinear models to linear ones based on the(More)
—In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically(More)
Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of(More)
This paper presents a novel approach using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS) for estimation of blood pressure (BP) from oscillometric waveforms. The proposed method consists of three stages. In the first stage, the oscillation amplitudes (OAs) of the oscillometric waveforms are represented as a function of(More)
Automatic classification of lung tissue patterns in high resolution computed tomography images of patients with interstitial lung diseases is an important stage in the construction of a computer-aided diagnosis system. To this end, a novel approach is proposed using two sets of overcomplete wavelet filters, namely discrete wavelet frames (DWF) and rotated(More)
Fuzzy c-mean (FCM) is a common clustering algorithm which is used for segmentation of magnetic resonance (MR) images. However in the case of noisy MR images, efficiency of this algorithm considerably reduces. Recently, researchers have been introduced two new parameters in order to improve performance of traditional FCM in the case of noisy images. New(More)
In this paper, a mathematical model for the blood pressure oscillometric waveform (OMW) is developed and a statespace approach using the extended Kalman filter (EKF) is proposed to adaptively estimate and track parameters of clinical interest. The OMW model is driven by a previously proposed pressure-lumen area model of the artery under the deflating cuff.(More)