A Flexible Metamaterial Based Printed Antenna for Wearable Biomedical Applications.

This paper presents a microstrip antenna based on metamaterials (MTM). The proposed antenna showed several resonances around the BAN and ISM frequency bands. The antenna showed a suitable gain for short and medium wireless communication systems of about 1 dBi, 1.24 dBi, 1.48 dBi, 2.05 dBi, and 4.11 dBi at 403 MHz, 433 MH, 611 Mz, 912 MHz, and 2.45 GHz, respectively. The antenna was printed using silver nanoparticle ink on a polymer substrate. The antenna size was reduced to 20 × 10 mm2 to suit the different miniaturized wireless biomedical devices. The fabricated prototype was tested experimentally on the human body. The main novelty with this design is its ability to suppress the surface wave from the patch edges, significantly reducing the back radiation toward the human body when used close to it. The antenna was located on the human head to specify the specific absorption rate (SAR). It was found in all cases that the proposed antenna showed low SAR effects on the human body.

A Three-Dimensional Finite Element Analysis of Displacement and Stress Distributions of Unilateral and Bilateral Cleft Lips by Using Developed Pre-Surgical Treatment Architecture.

Cleft lips and cleft palates are the most common birth defects in newborns. Pre-surgical correction of unilateral and bilateral cleft lips and palates has been the subject of interest of many previous works. This condition has necessitated the evolution of many surgical and non-surgical techniques to mitigate the problem of this deformity in children. In this study, we proposed a new architecture that can be used instead of the conventional pre-surgical treatment. The proposed architecture has mechanical and electronic parts. This architecture was adopted to apply external stress to the cleft bones and cleft edges using an airbag that is located in the mechanical part. The amount of air in the airbag can be controlled by an available control unit in the electronic part. The effect of external stress on the cleft bones and the cleft edges was analyzed by using the finite element analysis (FEA) method. The FEA study aimed to analyze the displacement, amount of tensile and compressive forces, and Von Mises stress distributions on the cleft bones, cleft edges, nasal septum, and superior alveolar part of the maxillary jaw of unilateral and bilateral cleft models during pre-surgical treatment with the novel architecture. The results show that displacement and stress affected the clefts of both models. Displacement had a significant effect of gradually bringing the clefts closer to each other and returning them to the posterior. The analysis also investigated the effects of stress on the cleft bone and cleft edge. It was found from the results that the stresses helped to bring the incisions closer to the most appropriate position for plastic surgeons. The results prove that the positive and negative X-displacements move in the opposite direction, which means that the cleft edges gradually converge toward each other. Moreover, the negative Z-displacement affected the movement of cleft bones and cleft edges from outside to inside and gradually returned them to a suitable position. The findings show that the proposed architecture can be contributed to the pre-surgical treatment of the unilateral and bilateral clefts as an alternative to the traditional method.

Assessment of an in-house phantom for the quality control of a clinical gamma camera.

OBJECTIVE: Since in-house phantoms may provide effective quality control for gamma cameras in clinical settings, this study aims to assess an in-house phantom designed to perform quality control tests of a gamma camera using locally available, affordable materials. This is of particular importance in developing countries where scientific support may not be readily available. MATERIALS AND METHODS: The phantom was made from cylindrical plexiglass with a diameter of 230 mm and thickness of 60 mm. The phantom design was based on NEMA recommendations and only used materials that are locally available and generally accessible to most nuclear medicine departments and require minimal engineering instruction. RESULTS: The phantom demonstrated high levels of reliability and accuracy. The integral uniformity range was between 1.93% and 2.40%. The differential uniformity ranged between 1.48% and 1.70%. CONCLUSION: This work demonstrates that in-house phantoms are capable of monitoring gamma camera performance. This approach is particularly useful when scientific support is not easily accessible and when commercial phantoms are not readily available.

Algorithm for automatic recognition of PSK and QAM with unique classifier based on features and threshold levels.

In this paper, we present a unique modulation classification method that is based on determining an attractive relation between higher-order cumulants (HOCs) using a decision tree-classifier to improve the extracted features employed for the recognition of modulation schemes, such as phase shift keying (PSK) and quadrature amplitude modulation (QAM). A threshold algorithm is applied to the proposed classifier, which consists of sub-classifiers, each comprising a single feature, and each being capable of distinguishing the modulation types individually. In this work, a high-accuracy classifier system is utilized to recognize modulation schemes, such as QAM (16, 32, 64, 128, and 256) and (2, 4, and 8) PSK at a low signal-to-noise ratio (SNR). In this study, 1000 signals are studied for each SNR of -5 dB to 30 dB. The most prominent results of the classifier decisions range from 88% to 100% with regard to distinguishing the same types of PSK and QAM. In the long run, the proposed classifier module will be advantageous in terms of accuracy and computational complexity relative to the other classifiers in the literature. The results demonstrate that the proposed algorithm has a significantly better classification accuracy in comparison with the previously proposed ones.

Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection.

Electroencephalography is an important clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, we have proposed subspace-based methods to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. The variations in the shape of the EEG power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of epileptic seizure. Global performance of the proposed methods was evaluated by means of the visual inspection of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of the autoregressive techniques were given. The results demonstrate consistently superior performance of the proposed methods over the autoregressive ones.

Classification of EEG signals using neural network and logistic regression.

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.

Electrocardiogram signals de-noising using lifting-based discrete wavelet transform.

This paper introduces an effective technique for the denoising of electrocardiogram (ECG) signals corrupted by nonstationary noises. The technique is based on a second generation wavelet transform and level-dependent threshold estimator. Here, wavelet coefficients of ECG signals were obtained with lifting-based wavelet filters. A lifting scheme is used to construct second-generation wavelets and is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method is considered in relation to several measuring parameters, including types of wavelet filters (Haar, Daubechies 4 (DB4), Daubechies 6 (DB6), Filter(9-7), and Cubic B-splines), thresholding method, and decomposition depth. Three different kinds of noise were considered in this work: muscle artifact noise, electrode motion artifact noise, and white noise. Global performance is evaluated by means of the signal-to-noise ratio and visual inspection. Numerical results comparing the performance of the proposed method with that of nonlinear filtering techniques (median filter) are given. The results demonstrate consistently superior denoising performance of the proposed method over median filtering.