Sharif Digital Repository

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal  

Several methods have been proposed to construct Statistical Shape Model (SSM) to aim image analysis using computer in field Computer Aided Diagnosis (CAD), Computer Assisted Surgery (CAS), and other medical applications by providing a prior knowledge. The major challenge for liver shape model is a high variation in geometry such as size, shape and volume between livers. In this paper, we have presented a new technique for the automatic Multi-Scale Statistical Shape Model (MS-SSM) of three-dimensional (3-D) liver from volumetric segmented images data. The procedure included both building of Spherical Harmonics shape description and the Wavelet transform. Principal Component Analysis (PCA) was... 

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also,... 

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are... 

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to... 

Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal. The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a... 

Several researches have been done to identify visual system characteristics. Some of them are based on the processing of the brain signal recordings. Visual evoked potentials (VEPs) are electrical signals which are produced in response to the visual stimuli and recorded by means of electrodes placed on the head. These signals are usually characterized by the amplitude and latency of their peaks. Different types of visual stimuli and visual system characteristics can affect the shape and hence the characteristics of VEPs. In this paper, proper visual stimuli were used and VEPs were recorded in order to classify visual acuity. To achieve this goal, visual evoked potentials were recorded and... 

Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities... 

We investigate the form of the image of a finite sized spherical particle in confocal and conventional microscopes when the illuminating light has an arbitrary polarization. In particular, we take the cases of radial and azimuthal polarizations and use the Mie theory to find the scattered field from differently sized particles for these cases. We present numerical results for the changes in the detected intensity when subresolution and resolvable spherical particles are illuminated with particular wavelengths and polarizations. Further, we find the limiting size of a particle for which it can be considered a point scatterer for a particular wavelength. © 2008 Optical Society of America  

Coronavirus disease 2019 (COVID-19) is now of global concern due to its rapid dissemination across the globe. The rapid spread of this viral infection, along with many of its unknown aspects, has posed new challenges to the health care systems. The main challenging effects of COVID-19 are rapid dissemination through close contact and varying clinical severity among different individuals. Furthermore, the medical staff in endemic areas are becoming exhausted and deal with a considerable level of job burnout, which can negatively affect their medical decision making. Also, due to the variable pulmonary manifestations of COVID-19, some physicians may misdiagnose patients. To overcome these... 

Background: Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution. New method: This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately. Results: The method is applied on the synthetic and real DWI data of control and... 

Goal: Continuous blood pressure (BP) monitoring can provide invaluable information about individuals' health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This paper presents an efficient algorithm, based on the pulse arrival time (PAT), for the continuous and cuffless estimation of the systolic BP, diastolic blood pressure (DBP), and mean arterial pressure (MAP) values. Methods: The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally,... 

Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the... 

Background: Functional knee braces are extensively used for partially and completely torn anterior cruciate ligament (ACL) patients and those who have undergone ACL graft reconstruction, in order to support the healing ACL, improve the joint's functional stability, and restore the normal joint kinematics. Research question: Does wearing braces alter the arthrokinematics of the ACL deficient knees during lung exercise? Methods: For ten male unilateral ACL deficient subjects, 3D knee models were reconstructed from CT images, acquired in rest position. Sagittal plane fluoroscopy was then performed throughout a complete cycle of lunge in braced and non-braced conditions. The 3D kinematics of the... 

In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (RSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any... 

Melanoma is the deadliest type of human skin cancer. However, it is curable if diagnosed in an early stage. Recently, computer aided diagnosis (CAD) systems have drawn much interests. Segmentation is a crucial step of a CAD system. There are different types of skin lesions having high similarities in terms of color, shape, size and appearance. Most available works focus on a binary segmentation. Due to the huge variety of skin lesions and high similarities between different types of lesions, multi-class segmentation is still a challenging task. Here, we propose a method based on joint dictionary learning for multi-class segmentation of dermoscopic images. The key idea is based on combining...