Incremental Local Linear Fuzzy Classifier in Fisher Space

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort

CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans

Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Semantic segmentation methods using deep learning have exhibited top-tier performance in recent years, however designing accurate and robust segmentation models for lung tissue is challenging due to the variations in shape, size, and orientation. Additionally, medical image artifacts and noise can affect lung tissue segmentation and degrade the accuracy of downstream analysis. The practicality of current deep learning methods for lung tissue segmentation is limited as they require significant computational resources and may not be easily deployable in clinical settings. This paper presents a fully automatic method that identifies the lungs in three-dimensional (3D) pulmonary CT images using deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional image representation from consecutive CT slices that succinctly represents volumetric information and (2) a U-Net architecture equipped with pre-trained InceptionV3 blocks to segment 3D CT scans while maintaining the number of learnable parameters as low as possible. Our method was quantitatively assessed using one public dataset, LUNA16, for training and testing and two public datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of learnable parameters, our method achieved high generalizability to the unseen VESSEL12 and CRPF datasets while obtaining superior performance over Luna16 compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly accessible via a graphical user interface at medvispy.ee.kntu.ac.ir

Human Breathing Rate Estimation from Radar Returns Using Harmonically Related Filters

Radar-based noncontact sensing of life sign signals is often used in safety and rescue missions during disasters such as earthquakes and avalanches and for home care applications. The radar returns obtained from a human target contain the breathing frequency along with its strong higher harmonics depending on the target’s posture. As a consequence, well understood, computationally efficient, and the most popular traditional FFT-based estimators that rely only on the strongest peak for estimates of breathing rates may be inaccurate. The paper proposes a solution for correcting the estimation errors of such single peak-based algorithms. The proposed method is based on using harmonically related comb filters over a set of all possible breathing frequencies. The method is tested on three subjects for different postures, for different distances between the radar and the subject, and for two different radar platforms: PN-UWB and phase modulated-CW (PM-CW) radars. Simplified algorithms more suitable for real-time implementation have also been proposed and compared using accuracy and computational complexity. The proposed breathing rate estimation algorithms provide a reduction of about 81% and 80% in the mean absolute error of breathing rates in comparison to the traditional FFT-based methods using strongest peak detection, for PN-UWB and PM-CW radars, respectively

Adaptive neuro-fuzzy inference system for oscillometric blood pressure estimation

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 the cuff pressure. In the second stage, the PCA is utilized to reduce the dimensionality of the input space by extracting the most effective features from the OAs. Finally, in the thir

Feature-based neural network approach for oscillometric blood pressure estimation

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 modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg-Marquar

Oscillometric blood pressure estimation using principal component analysis and neural networks

Estimation of systolic and diastolic pressures from the oscillometric waveform is a challenging task in noninvasive electronic blood pressure (BP) monitoring devices. Since the conventional oscillometric algorithms cannot model and extract the complex and nonlinear relationship that may exist between BP and oscillometric waveform, artificial neural networks (NNs) have been proposed as a possible alternative. However, the research on this topic has been limited to some simple architectures that directly estimate the BP from raw oscillatio

Bayesian fusion algorithm for improved oscillometric blood pressure estimation

A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively