Tahereh Kamali

All Publications

• Discovering Density-Based Clustering Structures Using Neighborhood Distance Entropy Consistency IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS Kamali, T., Stashuk, D. W. 2020; 7 (4): 1069?80

  View details for DOI 10.1109/TCSS.2020.3003538

  View details for Web of Science ID 000557355100020

• Diagnosis of Myotonic Dystrophy Based on Resting State fMRI Using Convolutional Neural Networks. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Kamali, T., Hagerman, K. A., Day, J. W., Sampson, J., Lim, K. O., Mueller, B. A., Wozniak, J. 2020; 2020: 1714?17

  Abstract

  Myotonic dystrophies (DM) are neuromuscular conditions that cause widespread effects throughout the body. There are brain white matter changes on MRI in patients with DM that correlate with neuropsychological functional changes. How these brain alterations causally relate to the presence and severity of cognitive symptoms remains largely unknown. Deep neural networks have significantly improved the performance of image classification of huge datasets. However, its application in brain imaging is limited and not well described, due to the scarcity of labeled training data. In this work, we propose an approach for the diagnosis of DM based on a spatio-temporal deep learning paradigm. The obtained accuracy (73.71%) and sensitivities and specificities showed that the implemented approach based on 4-D convolutional neural networks leads to a compact, discriminative, and fast computing DM-based clinical medical decision support system.Clinical relevance- Many adults with DM experience cognitive and neurological effects impacting their quality of life, and ability to maintain employment. A robust and reliable DM-based clinical decision support system may help reduce the long diagnostic delay common to DM. Furthermore, it can help neurologists better understand the pathophysiology of the disease and analyze effects of new drugs that aim to address the neurological symptoms of DM.

  View details for DOI 10.1109/EMBC44109.2020.9176455

  View details for PubMedID 33018327

• Transparent Electrophysiological Muscle Classification From EMG Signals Using Fuzzy-Based Multiple Instance Learning IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Kamali, T., Stashuk, D. W. 2020; 28 (4): 842?49

  Abstract

  Although a well-established body of literature has examined electrophysiological muscle classification methods and systems, ways to enhance their transparency is still an important challenge and requires further study. In this work, a transparent semi-supervised electrophysiological muscle classification system which uses needle-detected EMG signals to classify muscles as normal, myopathic, or neurogenic is proposed. The electrophysiological muscle classification (EMC) problem is naturally formulated using multiple instance learning (MIL) and needs an adaptation of standard supervised classifiers for the purpose of training and evaluating bags of instances. Here, a novel MIL-based EMC system in which the muscle classifier uses predictions based on motor unit potentials (MUPs) to infer muscle labels is described. This system uses morphological, stability, near fiber and spectral MUP features. Quantitative results obtained from applying the proposed transparent system to four electrophysiologically different groups of muscles, composed of proximal and distal hand and leg muscles, resulted in an average classification accuracy of 95.85%. The findings show the superior and stable performance of the proposed EMC system compared to previous works using other supervised, semi-supervised and unsupervised methods.

  View details for DOI 10.1109/TNSRE.2020.2979412

  View details for Web of Science ID 000527793800008

  View details for PubMedID 32149647

• Electrophysiological Muscle Classification Using Multiple Instance Learning and Unsupervised Time and Spectral Domain Analysis IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Kamali, T., Stashuk, D. W. 2018; 65 (11): 2494?2502

  Abstract

  Electrophysiological muscle classification (EMC) is a crucial step in the diagnosis of neuromuscular disorders. Existing quantitative techniques are not sufficiently robust and accurate to be reliably clinically used. Here, EMC is modeled as a multiple instance learning (MIL) problem and a system to infer unsupervised motor unit potential (MUP) labels and create supervised muscle classifications is presented.The system has five main steps: MUP representation using morphological, stability, and near fiber parameters as well as spectral features extracted from wavelet coefficients; MUP feature selection using unsupervised Laplacian scores; MUP clustering using neighborhood distance entropy consistency to find representations of MUP normality and abnormality; muscle representation by embedding its MUP cluster associations in a feature vector; and muscle classification using support vector machines or random forests.The evaluation data consist of 63, 83, 93, and 84 sets of MUPs recorded in deltoid, vastus medialis, first dorsal interosseous, and tibialis anterior muscles, respectively. The proposed system discovered representations of normal, myopathic, and neurogenic MUPs for each specific muscle type and resulted in an average classification accuracy of 98%, which is higher than in previous works.Modeling EMC as an instance of the MIL solves the traditional problem of characterizing MUPs without full supervision. Furthermore, finding representations of MUP normality and abnormality using morphological, stability, near fiber, and spectral features improve muscle classification.The proposed method is able to characterize MUPs with respect to disease categories, with no a priori information.

  View details for DOI 10.1109/TBME.2018.2802200

  View details for Web of Science ID 000447801800012

  View details for PubMedID 29993485

• A Multiple Instance Learning Approach to Electrophysiological Muscle Classification for Diagnosing Neuromuscular Disorders Using Quantitative EMG Kamali, T. University of Waterloo. 2018
• A Density-Based Clustering Approach to Motor Unit Potential Characterizations to Support Diagnosis of Neuromuscular Disorders IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Kamali, T., Stashuk, D. W. 2017; 25 (7): 956?66

  Abstract

  Electrophysiological muscle classification involves characterization of extracted motor unit potentials (MUPs) followed by the aggregation of these MUP characterizations. Existing techniques consider three classes (i.e., myopathic, neurogenic, and normal) for both MUP characterization and electrophysiological muscle classification. However, diseased-induced MUP changes are continuous in nature, which make it difficult to find distinct boundaries between normal, myopathic, and neurogenic MUPs. Hence, MUP characterization based on more than three classes is better able to represent the various effects of disease. Here, a novel, electrophysio- logical muscle classification system is proposed, which considers a dynamic number of classes for characterizing MUPs. To this end, a clustering algorithm called neighbor- hood distances entropy consistency is proposed to find clusters with arbitrary shapes and densities in an MUP feature space. These clusters represent several concepts of MUP normality and abnormality and are used for MUP characterization instead of the conventional three classes. An examined muscle is then classified by embedding its MUP characterizations in a feature vector fed to an ensemble of support vector machine and nearest neighbor classifiers. For 103 sets of MUPs recorded in tibialis anterior muscles, the proposed system had a 97% electro-physiological muscle classification accuracy, which is significantly higher than in previous works.

  View details for DOI 10.1109/TNSRE.2017.2673664

  View details for Web of Science ID 000407431300017

  View details for PubMedID 28252410

• Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm ARTIFICIAL INTELLIGENCE IN MEDICINE Kamali, T., Stashuk, D. 2016; 73: 14?22

  Abstract

  Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions.Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data.The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71).NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity.

  View details for DOI 10.1016/j.artmed.2016.09.003

  View details for Web of Science ID 000388046500002

  View details for PubMedID 27926378

• A Multi-Classifier Approach to MUAP Classification for Diagnosis of Neuromuscular Disorders IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Kamali, T., Boostani, R., Parsaei, H. 2014; 22 (1): 191?200

  Abstract

  The shapes and sounds of isolated motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. These parameters can be analyzed qualitatively by an expert or quantitatively by using pattern recognition techniques. Due to the advantages of quantitative EMG method, developing robust automated MUAP classifiers have been explored and several systems have been developed for this purpose by now, but the accuracy of the existing methods is not high enough to be used in clinical environments. In this paper, a novel classification strategy based on ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture is proposed to determine the class label (myopathic, neuropathic, or normal) for a given MUAP. The developed system employs both time domain and time-frequency domain features of the MUAPs extracted from an EMG signal using an EMG signal decomposition system. Different classification strategies including single classifier and multiple classifiers with several subsets of features were investigated. Experimental results using a set of real EMG signals showed robust performance of multi-classifier methods proposed here. Of the methods studied, the multi-classifier that uses multiple features sets and a combination of both trainable and nontrainable fusion techniques to aggregate base classifiers showed the best performance with average accuracy of 97% which is significantly higher than the average accuracy of single SVM-based classifier system (i.e., 88%).

  View details for DOI 10.1109/TNSRE.2013.2291322

  View details for Web of Science ID 000329876500020

  View details for PubMedID 24263096

• A Hybrid Classifier for Characterizing Motor Unit Action Potentials in Diagnosing Neuromuscular Disorders Journal of Biomedical Physics & Engineering Kamali, T., Boostani, R., Parsaei, H. 2013