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   Table of Contents - Current issue
July-September 2021
Volume 11 | Issue 3
Page Nos. 159-226

Online since Wednesday, July 21, 2021

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Residual learning: A new paradigm to improve deep learning-based segmentation of the left ventricle in magnetic resonance imaging cardiac images p. 159
Maral Zarvani, Sara Saberi, Reza Azmi, Seyed Vahab Shojaedini
Background: Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate segmentation of the heart in captured images. Although various schemes have been tested for this segmentation so far, the latest proposed methods have used the concept of deep learning to estimate the range of the left ventricle in cardiac MRI images. While deep learning methods can lead to better results than their classical alternatives, but unfortunately, the gradient vanishing and exploding problems may hamper their efficiency for the accurate segmentation of the left ventricle in MRI heart images. Methods: In this article, a new concept called residual learning is utilized to improve the performance of deep learning schemes against gradient vanishing problems. For this purpose, the Residual Network of Residual Network (i.e., Residual of Residual) substructure is utilized inside the main deep learning architecture (e.g., Unet), which provides more significant detection indexes. Results and Conclusion: The proposed method's performances and its alternatives were evaluated on Sunnybrook Cardiac Data as a reliable dataset in the left ventricle segmentation. The results show that the detection parameters are improved at least by 5%, 3.5%, 8.1%, and 11.4% compared to its deep alternatives in terms of Jaccard, Dice, precision, and false-positive rate indexes, respectively. These improvements were made when the recall parameter was reduced to a negligible value (i.e., approximately 1%). Overall, the proposed method can be used as a suitable tool for more accurate detection of the left ventricle in MRI images.
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A fast approximate method for predicting the behavior of auditory nerve fibers and the evoked compound action potential (ECAP) signal p. 169
Azam Ghanaei, S Mohammad Firoozabadi, Hamed Sadjedi
Background: The goal of the current research is to develop a model based on computer simulations which describes both the behavior of the auditory nerve fibers and the cochlear implant system as a rehabilitation device. Methods: The approximate method was proposed as a low error and fast tool for predicting the behavior of auditory nerve fibers as well as the evoked compound action potential (ECAP) signal. In accurate methods every fiber is simulated; whereas, in approximate method information related to the response of every fiber and its characteristics such as the activation threshold of cochlear fibers are saved and interpolated to predict the behavior of a set of nerve fibers. Results: The approximate model can predict and analyze different stimulation techniques. Although precision is reduced to <1.66% of the accurate method, the required execution time for simulation is reduced by more than 98%. Conclusion: The amplitudes of the ECAP signal and the growth function were investigated by changing the parameters of the approximate model including geometrical parameters, electrical, and temporal parameters. In practice, an audiologist can tune the stimulation parameters to reach an effective restoration of the acoustic signal.
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Speckle tracking accuracy enhancement by temporal super-resolution of three-dimensional echocardiography images p. 177
Mohammad Jalali, Hamid Behnam
Background: Speckle tracking has always been a challenging issue in echocardiography images due to the lowcontrast and noisy nature of ultrasonic imaging modality. While in ultrasound imaging, framerate is limited by image size and sound speed in tissue, speckle tracking results get worse inthree-dimensional imaging due to its lower frame rate. Therefore, numerous techniques have beenreported to overcome this limitation and enhance tracking accuracy. Methods: In this work, we have proposedto increase the frame rate temporally for a sequence of three-dimensional (3D) echocardiographyframes to make tracking more accurate. To increase the number of frames, cubic B-spline is usedto interpolate between intensity variation time curves extracted from every single voxel in theimage during the cardiac cycle. We have shown that the frame rate increase will result in trackingaccuracy improvement. Results: To prove the efficiency of the proposed method, numerical evaluation metricsfor tracking are reported to make a comparison between high temporal resolution sequences andlow temporal resolution sequences. Anatomical affine optical flow is selected as the state-of-the-artspeckle tracking method, and a 3D echocardiography dataset is used to evaluate the proposedmethod. Conclusion: Results show that it is beneficial for speckle tracking to perform on temporally condensedframes rather than ordinary clinical 3D echocardiography images. Normalized mean enhancementvalues for mean absolute error, Hausdorff distance, and Dice index for all cases and all frames are0.44 ± 0.09, 0.42± 0.09, and 0.36 ± 0.06, respectively.
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Detection and classification of myocardial infarction with support vector machine classifier using grasshopper optimization algorithm p. 185
Naser Safdarian, Shadi Yoosefian Nezhad, Nader Jafarnia Dabanloo
Background: Providing a noninvasive, rapid, and cost-effective approach to diagnose of myocardial infarction (MI) is essential in the early stages of electrocardiogram (ECG) signaling. In this article, we proposed the new optimization method for support vector machine (SVM) classifier to MI classification. Methods: After preprocessing ECG signal and noise removal, three features such as Q-wave integral, T-wave integral, and QRS-complex integral have been extracted in this study. After that, different statistical tests have evaluated the matrix of these features. To more accurately detect and classify the MI disease, optimizing the SVM classification parameters using the grasshopper optimization algorithm (GOA) was first used in this study (that called SVM-GOA). Results: After applying the GOA on the SVM classifier for all three kernels, the final results of MI detection for sensitivity, specificity, and accuracy were 100% ± 0%, 100% ± 0%, and 100% ± 0%, respectively. The final results of different MI types' classification after applying the GOA on SVM for polynomial kernel were obtained 100% ± 0%, 97.37% ± 0%, and 94.2% ± 0.2% for sensitivity and specificity and accuracy, respectively. However, the results of both linear and RBF kernels that were used for the SVM classifier method have also shown a significant increase after using GOA. Conclusion: This article's results show the highly desirable effect of applying a GOA to optimize different kernel parameters used in the SVM classifier for accurate detection and classification of MI. The proposed algorithm's final results show that the proposed system has a relatively higher performance than other previous studies.
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Automatic generation of structured radiology reports for volumetric computed tomography images using question-Specific deep feature extraction and learning p. 194
Samira Loveymi, Mir Hossein Dezfoulian, Muharram Mansoorizadeh
Background: In today's modern medicine, the use of radiological imaging devices has spread at medical centers. Therefore, the need for accurate, reliable, and portable medical image analysis and understanding systems has been increasing constantly. Accompanying images with the required clinical information, in the form of structured reports, is very important, because images play a pivotal role in detect, planning, and diagnosis of different diseases. Report-writing can be exposure to error, tedious and labor-intensive for physicians and radiologists; to address these issues, there is a need for systems that generate medical image reports automatically and efficiently. Thus, automatic report generation systems are among the most desired applications. Methods: This research proposes an automatic structured-radiology report generation system that is based on deep learning methods. Extracting useful and descriptive image features to model the conceptual contents of the images is one of the main challenges in this regard. Considering the ability of deep neural networks (DNNs) in soliciting informative and effective features as well as lower resource requirements, tailored convolutional neural networks and MobileNets are employed as the main building blocks of the proposed system. To cope with challenges such as multi-slice medical images and diversity of questions asked in a radiology report, our system develops volume-level and question-specific deep features using DNNs. Results: We demonstrate the effectiveness of the proposed system on ImageCLEF2015 Liver computed tomography (CT) annotation task, for filling in a structured radiology report about liver CT. The results confirm the efficiency of the proposed approach, as compared to classic annotation methods. Conclusion: We have proposed a question-specific DNN-based system for filling in structured radiology reports about medical images.
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Design and development armband vital sign monitor for health-care monitoring p. 208
Sugondo Hadiyoso, Rohmat Tulloh, Yuyun Siti Rohmah, Akhmad Alfaruq
Background: One of the vital organs that require regular check is heart. The representation of heart health can be identified through electrocardiogram (ECG) signals, blood pressure (BP), heart rate, and oxygen saturation (SpO2). Monitoring the heart condition needs to be regularly done to prevent heart attack that can occur suddenly and very quickly particularly for someone who has had a heart attack before. Nevertheless, it raises the problem of cost, time efficient, and flexibility. It takes a high cost and much time to perform this examination. A vital signal monitoring device is needed with low cost, wearable, accurate, and simple in use. Methods: This research designs and develops a device and application for monitoring human vital signals including ECG, SpO2, BP, and heart rate. A multi-sensor system with a control unit was applied to the device which was then called the Armband Vital Sign Monitor. This device can be used to measure vital parameters simultaneously using multiplexing techniques programmed in the microcontroller. Armband vital sign monitor is also equipped with Bluetooth module as a communication media for further data processing and display. Results: Armband vital sign monitor produces >99% accuracy in body temperature measurements, ±2 deviation values in SpO2 measurements, and systolic and diastolic deviations at ±3–8 mmHg. For EGC signals, tests are performed by comparing signals visually in graphical form, and EGC can be obtained properly as shown by the graph. Conclusion: In this study, an Armband vital sign device has been developed that can measure the body's vital parameters. The parameters which were measured included temperature, heart rate, BP, SpO2, and ECG. This device has small dimensions and can be put on the wrist. The device is also equipped with Bluetooth so monitoring can be conducted wirelessly.
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Designing a glass mounted warning system to prevent drivers to fall in sleep based on neck posture and blinking duration p. 217
Niloufar Teyfour, Hossein Shirvani, Alireza Shamsoddini
Background: In this study, an electronic system based on driver's neck position and blinking duration is designed to help prevent car crashed due to driver drowsiness. When a driver falls in sleep his/her head is felled down. Hence, driver's neck posture can be a good sign of sleep which is measured utilizing a two?dimensional accelerator. However, this sign is not enough because he/she may need to look down during a drive and alarming driver by every moving down of head can be annoying. Methods: Thus, in this system, we used blinking duration too. When a person is awake, blinks more frequently than when he is drowsy. Result: As a result, in this system, blinking is detected using an infrared transceiver and if both conditions, i.e., neck posture and blinking duration are showing signs of sleep mode, driver will be alarmed. Conclusion: In this study, it is designed 2D accelerometer and IR sensor based system to measure the driver's neck angle and detect driver's blinking to realize the drowsiness of vehicle drivers and alert them using these signs of drowsiness.
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Signal processing of heart rate for predicting sepsis in premature neonates p. 222
Kayvan Mirnia, Mohammad Heidarzadeh, Seyyed Abolfazl Afjeh, Parinaz Alizadeh, Abbas Abaei Kashan, Arash Bordbar, Amid Maghsoudi
The heart rate characteristic (HeRO score) is a figure derived from the analysis of premature neonate's electrocardiogram signals, and can be used to detect infection before the onset of clinical symptoms. The United States and Europe accept this diagnostic technique, but we require more tests to prove its efficacy. This method is not accepted in other developed countries so far. The present study aimed to investigate changes in the heart characteristics of two neonates in Akbar Abadi Hospital in Tehran. Experts chose one newborn as a sepsis case, and the other neonate was healthy. The results were analyzed and compared with previous studies. In this research, a group of five neonates was selected randomly from the neonatal intensive care unit, and cardiac leads were attached to them for recording heart rates. We selected two neonates from the five cases, as a case (proven sepsis) and control, to analyze heart rate variability (HRV). Then, we compared the differences in the heart rate of both neonates. Analysis of HRV of these two neonates showed that the pattern of HRV is compatible with reports from US studies. Considering the results of this study, heart rates and their analysis can provide useful indicators for mathematical modeling before the onset of clinical symptoms in newborns.
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