Maryam Ravan - Academia.edu (original) (raw)

Papers by Maryam Ravan

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough ... more Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough sleep at the right times can help protect mental health, physical health, quality of life, and safety. In this study, an electroencephalography (EEG)-based machine-learning approach is proposed to measure sleep quality. The advantages of our approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than five sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing EEG signals only using two EEG electrodes, so the user experience is improved because he/she is attached with fewer sensors during sleep. Using quantitative features obtained from EEG signals, we developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. We used polysomnographic data from PhysioBank database to train and evaluate the performance of the framework, where the sleep stages have been visually annotated. The results demonstrated that the proposed approach achieves high classification performance, which helps to measure sleep quality accurately.

Magnetism

Currently, there is a rapidly growing interest and demand for wearable textile sensors that can m... more Currently, there is a rapidly growing interest and demand for wearable textile sensors that can monitor human motions in a naturalistic environment. Some potential applications for this technology include research on measuring the motor skill performance of patients with motor disabilities such as autism spectrum disorder, Parkinson’s disease, cerebral palsy, and stroke and evaluating the efficacy of applied treatments. Among wearable sensors, inductive sensors that are made from highly conductive threads are attractive due to their easy development process, high reliability, and low cost. In this study, we analyzed and compared the performance of three inductive wearable sensor configurations—(1) single planar rectangular coil, (2) two separated coils connected in series, and (3) two helical coils connected in series—in terms of the change in the resonant frequency of the tank circuit they comprised as a result of the change in elbow joint angle through simulations. Three parameter...

Algorithms

Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though t... more Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results...

Magnetism, 2021

In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is... more In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is presented. By using customized sensor plates in single-ended and differential configurations, responses to hidden objects can be captured over a cylindrical aperture surrounding the inspected medium. Then, by processing the acquired data using a novel imaging technique based on the convolution theory, Fourier and inverse Fourier transforms, and exact low resolution electromagnetic tomography (eLORETA), images are reconstructed over multiple radial depths using the acquired sensor data. Imaging hidden objects over multiple depths has wide range of applications, from biomedical imaging to nondestructive testing of the materials. Performance of the proposed imaging technique is demonstrated via experimental results.

Electronics, 2021

The use of non-metallic pipes and composite components that are low-cost, durable, light-weight, ... more The use of non-metallic pipes and composite components that are low-cost, durable, light-weight, and resilient to corrosion is growing rapidly in various industrial sectors such as oil and gas industries in the form of non-metallic composite pipes. While these components are still prone to damages, traditional non-destructive testing (NDT) techniques such as eddy current technique and magnetic flux leakage technique cannot be utilized for inspection of these components. Microwave imaging can fill this gap as a favorable technique to perform inspection of non-metallic pipes. Holographic microwave imaging techniques are fast and robust and have been successfully employed in applications such as airport security screening and underground imaging. Here, we extend the use of holographic microwave imaging to inspection of multiple concentric pipes. To increase the speed of data acquisition, we utilize antenna arrays along the azimuthal direction in a cylindrical setup. A parametric study ...

2021 International Applied Computational Electromagnetics Society Symposium (ACES), 2021

In this paper, we propose a fast and low-cost cylindrical microwave imaging system based on the u... more In this paper, we propose a fast and low-cost cylindrical microwave imaging system based on the use of arrays of transmitter and receiver antennas and a customized low-cost data acquisition circuit using off-the-shelf components. The complex-valued scattered data captured with the proposed system is processed using near-field holographic image reconstruction. To enhance this technique, standardized minimum norm (SMN) approach is employed to solve the relevant systems of equations. The performance of the proposed imaging technique and the data acquisition system is demonstrated via simulations and experiments.

IEEE Transactions on Instrumentation and Measurement, 2021

Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless ... more Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless operation, low cost, and good penetration depth. To implement real-time imaging, in applications such as object tracking or functional biomedical imaging, fast data acquisition is required. To proceed toward this goal, in this article, we propose the use of a single coil for 3-D EII along with a fast inversion approach. To collect sufficient data that allow 3-D imaging, we propose two approaches: collecting data at multiple coil’s drive current levels and collecting data with multiple tank circuit’s capacitor values. We show that the use of such data when using a single coil allows for 3-D EII. For proof-of-concept experiments, we use a commercial data acquisition board, that is, LDC1614EVM module, and multilayer planar coils made by Texas Instruments.

2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2018

Currently, the common medical imaging systems operate based on X-rays, magnetic resonance imaging... more Currently, the common medical imaging systems operate based on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The performance of these systems varies in terms of resolution, cost, complexity, and health hazards. Microwave imaging is emerging as an alternative method based on nonionizing electromagnetic radiation. Microwave imaging has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, and cancer detection. Recently, three-dimensional (3D) near-field holographic imaging techniques have been developed for biomedical microwave imaging. These techniques require the acquisition of wideband data to perform 3D imaging. Requirement for wideband data imposes limitations on the hardware. It may also lead to errors in the produced images of dispersive tissues. To address these limitations, here, we propose a technique to perform 3D imaging with single frequency data. Instead of collecting data at multiple frequencies, we acquire the scattered fields with an array of resonant antennas. We demonstrate the possibility of 3D imaging with the proposed setup via simulation examples. To have a realistic study, the simulation data is contaminated by noise.

IEEE Transactions on Microwave Theory and Techniques, 2021

Holographic microwave imaging is fast and robust and it has been adapted for near-field applicati... more Holographic microwave imaging is fast and robust and it has been adapted for near-field applications such as biomedical imaging and nondestructive testing. While the imaging technique is fast, synthesizing a 2-D aperture by mechanical scanning of the antennas takes time. Here, antenna arrays are used to expedite data acquisition along the azimuthal direction in a cylindrical holographic near-field microwave imaging setup. To deal with the limited and nonuniform samples along the azimuthal direction, three holographic imaging approaches are evaluated, in which, we use interpolation, uniform or nonuniform discrete Fourier transform (DFT), standardized low-resolution brain electromagnetic tomography (sLORETA), and exact low-resolution brain electromagnetic tomography (eLORETA). Besides, to make the system low-cost and portable, off-the-shelf components are used to construct a data acquisition system replacing the vector network analyzers. Simulation and experimental studies are conducted to validate the performance of the proposed imaging system. Structural similarity (SSIM) index is used to assess the quality of the reconstructed images.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients wi... more Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps: 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

Electronics, 2020

In this paper, a novel methodology is proposed for material identification. It is based on the us... more In this paper, a novel methodology is proposed for material identification. It is based on the use of a microwave sensor array with the elements of the array resonating at various frequencies within a wide range and applying machine learning algorithms on the collected data. Unlike the previous microwave sensing systems which are mainly based on a single resonating sensor, the proposed methodology allows for material characterization over a wide frequency range which, in turn, improves the accuracy of the material identification procedure. The performance of the proposed methodology is tested via the use of easily available materials such as woods, cardboards, and plastics. However, the proposed methodology can be extended to other applications such as industrial liquid identification and composite material identification, among others.

IEEE Transactions on Instrumentation and Measurement, 2020

We develop a theory of algebraic operations over linear and context-free grammars that makes it p... more We develop a theory of algebraic operations over linear and context-free grammars that makes it possible to combine simple "atomic" grammars operating on single sequences into complex, multi-dimensional grammars. We demonstrate the utility of this framework by constructing the search spaces of complex alignment problems on multiple input sequences explicitly as algebraic expressions of very simple one-dimensional grammars. In particular, we provide a fully worked frameshift-aware, semiglobal DNA-protein alignment algorithm whose grammar is composed of products of small, atomic grammars. The compiler accompanying our theory makes it easy to experiment with the combination of multiple grammars and different operations. Composite grammars can be written out in L A T E X for documentation and as a guide to implementation of dynamic programming algorithms. An embedding in Haskell as a domain-specific language makes the theory directly accessible to writing and using grammar products without the detour of an external compiler. Software and supplemental files available here:

IEEE Transactions on Microwave Theory and Techniques, 2020

Conventional nondestructive testing techniques do not suffice to efficiently inspect the defects ... more Conventional nondestructive testing techniques do not suffice to efficiently inspect the defects in multiple nonmetallic pipes. Thus, in this article, we propose a novel method for inspection of these components based on near-field microwave holographic imaging augmented with beamspace transformation. This approach harnesses the ability of microwaves for 3-D imaging by measuring the backscattered fields in the near-field region. In the proposed technique, we augment the solution of near-field holographic imaging through the use of beamspace transformation which allows for focusing on each pipe during the solution process. This improves the quality of the reconstructed images, in particular, for the pipes which are further away from the measuring antennas. First, we demonstrate the performance of the proposed imaging technique using simulations. Then, we present the experimental results for imaging of concentric cylindrical polyvinyl chloride (PVC) pipes using wideband microwave data.

IEEE Magnetics Letters, 2020

In this letter, a novel approach is presented for imaging of metallic objects based on induction ... more In this letter, a novel approach is presented for imaging of metallic objects based on induction sensing. The approach relies on the concept of collection of the point-spread function (PSF) in a linear space-invariant system. For this purpose, the responses of small objects are measured a priori to serve as the PSFs of the imaging system. Then, these PSFs are employed in a test scenario in which the responses of unknown objects distributed over multiple depths are measured and inverted to reconstruct two-dimensional (2-D) images at those depths. The stack of these 2-D images provides a 3-D image. The image reconstruction is fast due to the use of forward and inverse Fourier transforms. This imaging approach is validated via the use of off-the-shelf components.

AIMS Electronics and Electrical Engineering, 2019

Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough ... more Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough sleep at the right times can help protect mental health, physical health, quality of life, and safety. In this study, an electroencephalography (EEG)-based machine-learning approach is proposed to measure sleep quality. The advantages of this approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than five sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing EEG signals only, so the user experience is improved because fewer sensors are attached to the body during sleep. Using quantitative features obtained from EEG signals, we developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. We used polysomnographic data from PhysioBank database to train and evaluate and test the performance of the framework, where the sleep stages have been visually annotated. The results demonstrated that the proposed approach achieves high classification performance, which helps to measure sleep quality accurately. This framework can provide a robust and accurate sleep quality assessment that helps clinicians to determine the presence and severity of sleep disorders, and also evaluate the efficacy of treatments.

Real‐Time Three‐Dimensional Imaging of Dielectric Bodies Using Microwave/Millimeter‐Wave Holography, 2019

International Journal of Antennas and Propagation, 2018

Three-dimensional (3D) microwave and millimeter wave imaging techniques based on the holographic ... more Three-dimensional (3D) microwave and millimeter wave imaging techniques based on the holographic principles have been successfully employed in several applications such as security screening, body shape measurement for the apparel industry, underground imaging, and wall imaging. The previously proposed 3D holographic imaging techniques require the acquisition of wideband data over rectangular or cylindrical apertures. Requirement for wideband data imposes limitations on the hardware (in particular at very high or very low frequencies). It may also lead to errors in the produced images if the media is dispersive (e.g., in biomedical imaging) and not modeled properly in the image reconstruction process. To address these limitations, here, we propose a technique to perform 3D imaging with single frequency data. Instead of collecting data at multiple frequencies, we acquire the backscattered fields with an array of resonant antennas. We demonstrate the possibility of 3D imaging with the...

IEEE Transactions on Biomedical Engineering, 2019

The goal of this work is to objectively evaluate the effectiveness of responsive (or closed-loop)... more The goal of this work is to objectively evaluate the effectiveness of responsive (or closed-loop) Vagus nerve stimulation (VNS) therapy in sleep quality in patients with medically refractory epilepsy. Methods: Using quantitative features obtained from electroencephalography (EEG), we first developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. To train and evaluate the performance of the framework, we used polysomnographic data of 23 healthy subjects from the PhysioBank database where the sleep stages have been visually annotated. We then used the trained classifier to label the sleep stages using data from 22 patients with epilepsy treated with short term responsive VNS Therapy during an epilepsy-monitoring unit (EMU) visit one month after VNS implantation and 10 VNS naive patients with epilepsy. Results: Application of multi-class SVM classifier to classify the three sleep stages of awake, light sleep + REM, and deep sleep achieved a classification accuracy of 90%. Results of application of this methodology to VNS treated and VNS naive patients revealed that patients treated with short term responsive VNS Therapy showed significant increase in sleep efficiency, and significant decrease in seizures plus interictal epileptiform discharges (IEDs) and awakenings. Conclusion: These results indicate that VNS treatment can reduce the epileptiform activities and thus help achieving better sleep quality for patients with epilepsy. Significance: The proposed approach can be used to investigate the effect of long-term VNS therapy on sleep quality.

Epilepsy research, 2017

VNS (Vagus Nerve Stimulation) Therapy(®) is an adjunctive therapy for patients with medically ref... more VNS (Vagus Nerve Stimulation) Therapy(®) is an adjunctive therapy for patients with medically refractory epilepsy. The primary metric used to assess response to any treatment for epilepsy is seizure frequency reduction as measured using seizure diaries. In addition to seizure frequency, reduction in seizure severity is clinically meaningful to patients and can be measured objectively. Analysis of electro-encephalographic (EEG) signals has revealed that seizures are accompanied by spatial synchronization of EEG electrodes that may persist for several minutes after the seizure. A quantitative feature was obtained from EEG data around ictal events collected during a 3-5day epilepsy monitoring unit (EMU) visit prior to VNS implantation and following one month after VNS implant. This feature was obtained from 15 patients who underwent implantation of the closed-loop AspireSR(®) VNS Therapy System. We used this feature to first evaluate if automated delivery of VNS at the time of seizure ...

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough ... more Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough sleep at the right times can help protect mental health, physical health, quality of life, and safety. In this study, an electroencephalography (EEG)-based machine-learning approach is proposed to measure sleep quality. The advantages of our approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than five sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing EEG signals only using two EEG electrodes, so the user experience is improved because he/she is attached with fewer sensors during sleep. Using quantitative features obtained from EEG signals, we developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. We used polysomnographic data from PhysioBank database to train and evaluate the performance of the framework, where the sleep stages have been visually annotated. The results demonstrated that the proposed approach achieves high classification performance, which helps to measure sleep quality accurately.

Magnetism

Currently, there is a rapidly growing interest and demand for wearable textile sensors that can m... more Currently, there is a rapidly growing interest and demand for wearable textile sensors that can monitor human motions in a naturalistic environment. Some potential applications for this technology include research on measuring the motor skill performance of patients with motor disabilities such as autism spectrum disorder, Parkinson’s disease, cerebral palsy, and stroke and evaluating the efficacy of applied treatments. Among wearable sensors, inductive sensors that are made from highly conductive threads are attractive due to their easy development process, high reliability, and low cost. In this study, we analyzed and compared the performance of three inductive wearable sensor configurations—(1) single planar rectangular coil, (2) two separated coils connected in series, and (3) two helical coils connected in series—in terms of the change in the resonant frequency of the tank circuit they comprised as a result of the change in elbow joint angle through simulations. Three parameter...

Algorithms

Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though t... more Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results...

Magnetism, 2021

In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is... more In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is presented. By using customized sensor plates in single-ended and differential configurations, responses to hidden objects can be captured over a cylindrical aperture surrounding the inspected medium. Then, by processing the acquired data using a novel imaging technique based on the convolution theory, Fourier and inverse Fourier transforms, and exact low resolution electromagnetic tomography (eLORETA), images are reconstructed over multiple radial depths using the acquired sensor data. Imaging hidden objects over multiple depths has wide range of applications, from biomedical imaging to nondestructive testing of the materials. Performance of the proposed imaging technique is demonstrated via experimental results.

Electronics, 2021

The use of non-metallic pipes and composite components that are low-cost, durable, light-weight, ... more The use of non-metallic pipes and composite components that are low-cost, durable, light-weight, and resilient to corrosion is growing rapidly in various industrial sectors such as oil and gas industries in the form of non-metallic composite pipes. While these components are still prone to damages, traditional non-destructive testing (NDT) techniques such as eddy current technique and magnetic flux leakage technique cannot be utilized for inspection of these components. Microwave imaging can fill this gap as a favorable technique to perform inspection of non-metallic pipes. Holographic microwave imaging techniques are fast and robust and have been successfully employed in applications such as airport security screening and underground imaging. Here, we extend the use of holographic microwave imaging to inspection of multiple concentric pipes. To increase the speed of data acquisition, we utilize antenna arrays along the azimuthal direction in a cylindrical setup. A parametric study ...

2021 International Applied Computational Electromagnetics Society Symposium (ACES), 2021

In this paper, we propose a fast and low-cost cylindrical microwave imaging system based on the u... more In this paper, we propose a fast and low-cost cylindrical microwave imaging system based on the use of arrays of transmitter and receiver antennas and a customized low-cost data acquisition circuit using off-the-shelf components. The complex-valued scattered data captured with the proposed system is processed using near-field holographic image reconstruction. To enhance this technique, standardized minimum norm (SMN) approach is employed to solve the relevant systems of equations. The performance of the proposed imaging technique and the data acquisition system is demonstrated via simulations and experiments.

IEEE Transactions on Instrumentation and Measurement, 2021

Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless ... more Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless operation, low cost, and good penetration depth. To implement real-time imaging, in applications such as object tracking or functional biomedical imaging, fast data acquisition is required. To proceed toward this goal, in this article, we propose the use of a single coil for 3-D EII along with a fast inversion approach. To collect sufficient data that allow 3-D imaging, we propose two approaches: collecting data at multiple coil’s drive current levels and collecting data with multiple tank circuit’s capacitor values. We show that the use of such data when using a single coil allows for 3-D EII. For proof-of-concept experiments, we use a commercial data acquisition board, that is, LDC1614EVM module, and multilayer planar coils made by Texas Instruments.

2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2018

Currently, the common medical imaging systems operate based on X-rays, magnetic resonance imaging... more Currently, the common medical imaging systems operate based on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The performance of these systems varies in terms of resolution, cost, complexity, and health hazards. Microwave imaging is emerging as an alternative method based on nonionizing electromagnetic radiation. Microwave imaging has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, and cancer detection. Recently, three-dimensional (3D) near-field holographic imaging techniques have been developed for biomedical microwave imaging. These techniques require the acquisition of wideband data to perform 3D imaging. Requirement for wideband data imposes limitations on the hardware. It may also lead to errors in the produced images of dispersive tissues. To address these limitations, here, we propose a technique to perform 3D imaging with single frequency data. Instead of collecting data at multiple frequencies, we acquire the scattered fields with an array of resonant antennas. We demonstrate the possibility of 3D imaging with the proposed setup via simulation examples. To have a realistic study, the simulation data is contaminated by noise.

IEEE Transactions on Microwave Theory and Techniques, 2021

Holographic microwave imaging is fast and robust and it has been adapted for near-field applicati... more Holographic microwave imaging is fast and robust and it has been adapted for near-field applications such as biomedical imaging and nondestructive testing. While the imaging technique is fast, synthesizing a 2-D aperture by mechanical scanning of the antennas takes time. Here, antenna arrays are used to expedite data acquisition along the azimuthal direction in a cylindrical holographic near-field microwave imaging setup. To deal with the limited and nonuniform samples along the azimuthal direction, three holographic imaging approaches are evaluated, in which, we use interpolation, uniform or nonuniform discrete Fourier transform (DFT), standardized low-resolution brain electromagnetic tomography (sLORETA), and exact low-resolution brain electromagnetic tomography (eLORETA). Besides, to make the system low-cost and portable, off-the-shelf components are used to construct a data acquisition system replacing the vector network analyzers. Simulation and experimental studies are conducted to validate the performance of the proposed imaging system. Structural similarity (SSIM) index is used to assess the quality of the reconstructed images.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients wi... more Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps: 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

Electronics, 2020

In this paper, a novel methodology is proposed for material identification. It is based on the us... more In this paper, a novel methodology is proposed for material identification. It is based on the use of a microwave sensor array with the elements of the array resonating at various frequencies within a wide range and applying machine learning algorithms on the collected data. Unlike the previous microwave sensing systems which are mainly based on a single resonating sensor, the proposed methodology allows for material characterization over a wide frequency range which, in turn, improves the accuracy of the material identification procedure. The performance of the proposed methodology is tested via the use of easily available materials such as woods, cardboards, and plastics. However, the proposed methodology can be extended to other applications such as industrial liquid identification and composite material identification, among others.

IEEE Transactions on Instrumentation and Measurement, 2020

We develop a theory of algebraic operations over linear and context-free grammars that makes it p... more We develop a theory of algebraic operations over linear and context-free grammars that makes it possible to combine simple "atomic" grammars operating on single sequences into complex, multi-dimensional grammars. We demonstrate the utility of this framework by constructing the search spaces of complex alignment problems on multiple input sequences explicitly as algebraic expressions of very simple one-dimensional grammars. In particular, we provide a fully worked frameshift-aware, semiglobal DNA-protein alignment algorithm whose grammar is composed of products of small, atomic grammars. The compiler accompanying our theory makes it easy to experiment with the combination of multiple grammars and different operations. Composite grammars can be written out in L A T E X for documentation and as a guide to implementation of dynamic programming algorithms. An embedding in Haskell as a domain-specific language makes the theory directly accessible to writing and using grammar products without the detour of an external compiler. Software and supplemental files available here:

IEEE Transactions on Microwave Theory and Techniques, 2020

Conventional nondestructive testing techniques do not suffice to efficiently inspect the defects ... more Conventional nondestructive testing techniques do not suffice to efficiently inspect the defects in multiple nonmetallic pipes. Thus, in this article, we propose a novel method for inspection of these components based on near-field microwave holographic imaging augmented with beamspace transformation. This approach harnesses the ability of microwaves for 3-D imaging by measuring the backscattered fields in the near-field region. In the proposed technique, we augment the solution of near-field holographic imaging through the use of beamspace transformation which allows for focusing on each pipe during the solution process. This improves the quality of the reconstructed images, in particular, for the pipes which are further away from the measuring antennas. First, we demonstrate the performance of the proposed imaging technique using simulations. Then, we present the experimental results for imaging of concentric cylindrical polyvinyl chloride (PVC) pipes using wideband microwave data.

IEEE Magnetics Letters, 2020

In this letter, a novel approach is presented for imaging of metallic objects based on induction ... more In this letter, a novel approach is presented for imaging of metallic objects based on induction sensing. The approach relies on the concept of collection of the point-spread function (PSF) in a linear space-invariant system. For this purpose, the responses of small objects are measured a priori to serve as the PSFs of the imaging system. Then, these PSFs are employed in a test scenario in which the responses of unknown objects distributed over multiple depths are measured and inverted to reconstruct two-dimensional (2-D) images at those depths. The stack of these 2-D images provides a 3-D image. The image reconstruction is fast due to the use of forward and inverse Fourier transforms. This imaging approach is validated via the use of off-the-shelf components.

AIMS Electronics and Electrical Engineering, 2019

Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough ... more Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough sleep at the right times can help protect mental health, physical health, quality of life, and safety. In this study, an electroencephalography (EEG)-based machine-learning approach is proposed to measure sleep quality. The advantages of this approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than five sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing EEG signals only, so the user experience is improved because fewer sensors are attached to the body during sleep. Using quantitative features obtained from EEG signals, we developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. We used polysomnographic data from PhysioBank database to train and evaluate and test the performance of the framework, where the sleep stages have been visually annotated. The results demonstrated that the proposed approach achieves high classification performance, which helps to measure sleep quality accurately. This framework can provide a robust and accurate sleep quality assessment that helps clinicians to determine the presence and severity of sleep disorders, and also evaluate the efficacy of treatments.

Real‐Time Three‐Dimensional Imaging of Dielectric Bodies Using Microwave/Millimeter‐Wave Holography, 2019

International Journal of Antennas and Propagation, 2018

Three-dimensional (3D) microwave and millimeter wave imaging techniques based on the holographic ... more Three-dimensional (3D) microwave and millimeter wave imaging techniques based on the holographic principles have been successfully employed in several applications such as security screening, body shape measurement for the apparel industry, underground imaging, and wall imaging. The previously proposed 3D holographic imaging techniques require the acquisition of wideband data over rectangular or cylindrical apertures. Requirement for wideband data imposes limitations on the hardware (in particular at very high or very low frequencies). It may also lead to errors in the produced images if the media is dispersive (e.g., in biomedical imaging) and not modeled properly in the image reconstruction process. To address these limitations, here, we propose a technique to perform 3D imaging with single frequency data. Instead of collecting data at multiple frequencies, we acquire the backscattered fields with an array of resonant antennas. We demonstrate the possibility of 3D imaging with the...

IEEE Transactions on Biomedical Engineering, 2019

The goal of this work is to objectively evaluate the effectiveness of responsive (or closed-loop)... more The goal of this work is to objectively evaluate the effectiveness of responsive (or closed-loop) Vagus nerve stimulation (VNS) therapy in sleep quality in patients with medically refractory epilepsy. Methods: Using quantitative features obtained from electroencephalography (EEG), we first developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. To train and evaluate the performance of the framework, we used polysomnographic data of 23 healthy subjects from the PhysioBank database where the sleep stages have been visually annotated. We then used the trained classifier to label the sleep stages using data from 22 patients with epilepsy treated with short term responsive VNS Therapy during an epilepsy-monitoring unit (EMU) visit one month after VNS implantation and 10 VNS naive patients with epilepsy. Results: Application of multi-class SVM classifier to classify the three sleep stages of awake, light sleep + REM, and deep sleep achieved a classification accuracy of 90%. Results of application of this methodology to VNS treated and VNS naive patients revealed that patients treated with short term responsive VNS Therapy showed significant increase in sleep efficiency, and significant decrease in seizures plus interictal epileptiform discharges (IEDs) and awakenings. Conclusion: These results indicate that VNS treatment can reduce the epileptiform activities and thus help achieving better sleep quality for patients with epilepsy. Significance: The proposed approach can be used to investigate the effect of long-term VNS therapy on sleep quality.

Epilepsy research, 2017

VNS (Vagus Nerve Stimulation) Therapy(®) is an adjunctive therapy for patients with medically ref... more VNS (Vagus Nerve Stimulation) Therapy(®) is an adjunctive therapy for patients with medically refractory epilepsy. The primary metric used to assess response to any treatment for epilepsy is seizure frequency reduction as measured using seizure diaries. In addition to seizure frequency, reduction in seizure severity is clinically meaningful to patients and can be measured objectively. Analysis of electro-encephalographic (EEG) signals has revealed that seizures are accompanied by spatial synchronization of EEG electrodes that may persist for several minutes after the seizure. A quantitative feature was obtained from EEG data around ictal events collected during a 3-5day epilepsy monitoring unit (EMU) visit prior to VNS implantation and following one month after VNS implant. This feature was obtained from 15 patients who underwent implantation of the closed-loop AspireSR(®) VNS Therapy System. We used this feature to first evaluate if automated delivery of VNS at the time of seizure ...