Full Research Paper
Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Saba Jaafari Kia; Hamid Behnam; Majid Vafaeezadeh; Ali Hosseinsabet
Volume 15, Issue 3 , December 2021, Pages 187-197
Abstract
Heart diseases are main cause factors endangering human health and life, one of the most important heart diseases is valvular heart disease, which has had an increasing trend in recent years. Therefore, if they are diagnosed and treated in time and correctly, they can improve the quality of life and ...
Read More
Heart diseases are main cause factors endangering human health and life, one of the most important heart diseases is valvular heart disease, which has had an increasing trend in recent years. Therefore, if they are diagnosed and treated in time and correctly, they can improve the quality of life and increase the life expectancy, so researchers have always been looking for ways to improve and accelerate the process of diagnosing this disease. Medical images monitoring and recording the activity of the human heart are the main ways to diagnose heart diseases. Processing of these images is generally complex and time consuming, so scientists and experts have always been looking for ways to speed up and facilitate the detection process. Manifold learning is one of the nonlinear dimension reduction methods which has different algorithms and can simplify the processing of echocardiographic images. In this study, using one of the manifold learning algorithms named LLE, we examined echocardiographic images of the heart, and tried to categorize groups with mitral disorders while identifying healthy data from those with disorders. Results show that the method has carefully separated the data of the healthy group from the group with the disorder, and good results were obtained in the data classification. The results show that more than 80% of the samples of the natural group have a different pattern in terms of manifold structure from the samples with the disorder.
Full Research Paper
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi
Volume 15, Issue 3 , December 2021, Pages 199-209
Abstract
The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. ...
Read More
The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. This model is the first discrete neuronal model with learning ability and shows complex and chaotic behaviors. The learning ability of this model has enabled it to simulate cognitive phenomena such as neuronal synchronization in near-realistic conditions. The model, which is derived from a simple three-layered feed-forward neural network, has several coexisting attractors that make learning possible in various basins of attraction. The study of model parameters shows that bifurcation occurs not only by changing the learning rate, but also external stimulation can change the model behavior and bifurcation pattern. This point that can be used in modeling and designing new therapies for cognitive disorders.
Full Research Paper
Biomedical Imaging / Medical Imaging
Farzaneh Keyvanfard; Alireza Rahiminasab; Abbas Nasiraei Moghaddam
Volume 15, Issue 3 , December 2021, Pages 211-220
Abstract
In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure ...
Read More
In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure and function. Investigation of brain variations in this disease has commonly been based on voxel-wise analysis or region-based studies. The aim of this study is to evaluate brain structure and function alterations in schizophrenia patients comparing to healthy control from the brain connectivity perspective. For this purpose, using the statistical test method, a comparison was made between all the structural and functional connections in the brain of 92 healthy individuals and 37 schizophrenia patients obtained from diffusion tensor imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) respectively. The findings of this study indicate that the number of altered edges in the brain functional network of patients is about 4 times more than the number of varied structural connections, which indicates the high impact of this disorder on brain function. Also, examination of the number of altered edges connected to each node, the affected areas in this disease were identified and it was shown that the schizophrenia patients’ brain has changed in parts of the brain subnetworks related to the default mode network (DMN), attention, somatomotor and vision networks. It was also shown that the altered brain structural connections of patients are involved in the areas of the superior frontal gyrus, temporal gyrus and part of the occipital cortex which are mostly shown relative increasing of the structural connectivity weights. The results of this study indicate the widespread effect of this disorder on the brain and suggest that the occurrence of some abnormal behaviors in schizophrenia patients may be due to some increased structural connectivity weights.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hamed Danandeh Hesar; Amin Danandeh Hesar
Volume 15, Issue 3 , December 2021, Pages 221-234
Abstract
Extended Kalman filter (EKF) is a well-known nonlinear Bayesian framework that has been deployed in various fields of ECG processing. However, it’s not very effective in removing non-stationary noises such as muscle artifacts (MA) which are common in ECG recordings. This paper addresses this issue ...
Read More
Extended Kalman filter (EKF) is a well-known nonlinear Bayesian framework that has been deployed in various fields of ECG processing. However, it’s not very effective in removing non-stationary noises such as muscle artifacts (MA) which are common in ECG recordings. This paper addresses this issue by proposing a new ECG dynamic model (EDM) and a novel formulation for EKF which improves its performance in non-stationary environments. In the new EDM, the measurement model is modified to include non-Gaussian, non-stationary additive noises as well as stationary ones. The proposed formulation for EKF algorithm in this paper enables it to perform better than standard EKF in removing non-stationary contaminants. The proposed filter also preserves the clinical characteristics of ECG signals better than standard EKF. In order to show the effectiveness of the proposed EKF algorithm, its denoising performance was evaluated on MIT-BIH Normal Sinus Rhythm database (NSRDB) in the presence of two different types of non-stationary contaminants; synthetic pink noise and real muscle artifact noise. The results showed that the proposed EKF framework in this paper has a significant outperformance over the standard EKF framework in non-stationary environments from both SNR improvement and MSEWPRD viewpoints.
Full Research Paper
Cognitive Biomedical Engineering
Zahra Soltanifar; Hamid Behnam; Anahita Khorrami Banaraki; Mojtaba Khodadadi; Behnoosh Hamed Ali; Ali Golbazi Mahdipour
Volume 15, Issue 3 , December 2021, Pages 235-246
Abstract
The pattern of abnormal gaze is observed in individuals with autism spectrum disorders. Studies of eye movements in people with autism have shown significant difference in the pattern of staring at the eyes and mouth compared to control groups. Yet, findings have been contradictory to date, and in spite ...
Read More
The pattern of abnormal gaze is observed in individuals with autism spectrum disorders. Studies of eye movements in people with autism have shown significant difference in the pattern of staring at the eyes and mouth compared to control groups. Yet, findings have been contradictory to date, and in spite of the fact that previous studies on eye dazzling in people with autism are expanding, the findings still do not appear to be consistent. Thus, we tracked eye movements in face processing for 25 teenagers with autism and 25 teenagers from the control group to examine any abnormal concentration in the facial areas. Experimental task used in this study includes standard images of the emotional states of the male and female faces (roundness of the face) in the state of anger, surprise, happiness, sadness and neutrality and subjects looked at these faces, while the eye tracker recorded their eye movements. In this task, they were required to select the displayed emotional state by the reply box. The selected Boosted Trees Ensemble classifier was able to use features related to the total data received from eye tracking in face segmentation into 8 areas (forehead, right and left eye, right and left cheek, nose, mouth and chin) with an accuracy of 83.31% in separating the two groups of autism and control. Moreover, in the study of facial components, left eye, left cheek, right cheek, and right eye, with 84.18%, 83.85%, 82.73% and 81.25% accuracy respectively, were able to make the most difference in the classification. Non-normal patterns in eye gaze can be very important because biomarkers indicate a condition that can be used for early diagnosis. It can also be a guide for researchers to design a game based on the results of this paper to improve the social interactions by strengthening eye contact for people with autism.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hadi Grailu
Volume 15, Issue 3 , December 2021, Pages 247-262
Abstract
Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated ...
Read More
Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated has increased and therefore, the storage and transmission of these signals has become a challenge. This, in turn, demonstrates the importance and necessity of using efficient methods for compression of these types of signals. In this paper, a lossy compression method is proposed for PCG signals recorded at a relatively high sampling rate so that it can control the quality of the compressed signal. This method is based on two techniques: "two-stage downsampling" and "pattern matching". The proposed two-stage downsampling technique increases the amount of compression ratio and at the same time reduces the computational complexity. The pattern matching technique is able to reduce the inter-period redundancy and therefore, increase the compression ratio. The simulation results of the proposed method on the two databases of the University of Michigan and the University of Washington showed that the two-stage downsampling and pattern matching techniques have a large contribution in increasing the compression ratio. The performance of the proposed method was evaluated according to the PRD and CR criteria and compared with that of some existing methods. In this evaluation, for the PRD range of 5%, the CR value was between 2500 and 3900 for the University of Michigan database and between 2500 and 4125 for the University of Washington database. Also, the results of applying the proposed method on the Pascal database showed that the efficiency of the proposed method depends to a large extent on the quality and regularity of the input PCG signals.
Full Research Paper
Dental Biomechanics
Pedram Akhlaghi; Setareh Khorshidparast; Gholamreza Rouhi
Volume 15, Issue 3 , December 2021, Pages 263-277
Abstract
Today, the success and failure of treatment by dental implants is influenced by the concept of primary and secondary stability. Primary stability is the capacity of the bone-implant system to withstand the loads, without noticeable damage to the adjacent bone, which may cause the implant to loosen, and ...
Read More
Today, the success and failure of treatment by dental implants is influenced by the concept of primary and secondary stability. Primary stability is the capacity of the bone-implant system to withstand the loads, without noticeable damage to the adjacent bone, which may cause the implant to loosen, and thus the implantation process fails. The aim of this study was to develop a micro-finite element (μFE) model and validate it with an in-vitro mechanical test, in order to evaluate the primary stability of dental implants by measuring the stiffness and ultimate load of the bone-implant system through cyclic compressive loading-unloading test. After bone-implant preparation, a quasi-static compressive step-wise loading-unloading cycles, with a displacement rate of 0.0024 mm/s and displacement-controlled were applied to the bone-implant structure with the amplitudes of 0.04 mm to 1.28 mm. Force-displacement curve and the stiffness of the structure in each step then were obtained. Prior to loading, the bony sample was scanned through a μCT device and a μFE model was developed based on the boundary and loading conditions similar to the in-vitro test to predict the force-displacement curve of the structure. Finally, the predicted force-displacement curve from μFE model was compared with the results of the experimental in-vitro test. Results showed that the predicted force-displacement curve from the μFE model is in agreement with the results of the experimental test. The μFE model developed here has the capability to show the overall response of the bone-implant structure under large deformations, and can also be used as a tool to improve the design of the dental implants, with the ultimate goal of increasing the stability of dental implants in immediate loading dental implants.
