Human Movement Modeling
Mehdi Yousefi Azar Khanian; Seyed Mohammad Reza Hashemi Golpayegani; Mostafa Rostami
Volume 13, Issue 1 , April 2019, , Pages 55-68
Abstract
Recently, analysis of the human postural stability has gained increasing interest. This is mainly due to the necessity of understanding the self-organization mechanisms in this system activated in response to any motion pattern. The extraction of effective indicators from this system could help ...
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Recently, analysis of the human postural stability has gained increasing interest. This is mainly due to the necessity of understanding the self-organization mechanisms in this system activated in response to any motion pattern. The extraction of effective indicators from this system could help clinicians to diagnose patients’ postural disorders and guide the rehabilitation processes. The center of pressure (CoP) signal, as a collective variable, contains information from the human equilibrium system. Through the CoP trajectory production, various control mechanisms are activated at different time intervals, which is equivalent with emerging different basin of attractors in the phase space. The dynamical coordination of this system patterns determines how system switches between these attractors. In this paper, first to quantify the local information of CoP, two indicators are defined; "local correlation dimension (LCD)" and "phase dynamic coordination (PDC)". Then, for a designed experiment, the local behavior pattern of CoP time series is calculated based on the suggested indicators. Next, by designing a model that can generate rich dynamics with multiple attractors, we attempt to follow data behavioral changes. The proposed model is map based. The model parameters are tuned by PCD to follow the pattern of sub-attractors changes with the system LCD. Tracking the behavioral patterns of the posture system is one of the prominent results of this research. The proposed model not only can follow the local behavior of system, but also follows the global dynamics. Accordingly, the similarity of the decreasing-increasing trend of the correlation dimension variations for the model output and data demonstrates the variations of system’s degrees of freedom in the test trials. The proposed model is the first behavioral model for the posture system, which can be used to quantify the variation of information in other biological systems based on the proposed methods.
Bioelectrics
Zahra Sadat Hosseini; Seyed Mohammad Reza Hashemi Golpayegani
Volume 13, Issue 1 , April 2019, , Pages 69-84
Abstract
The esophageal carcinoma is the eight most predominate malignancy in the world and the sixth deadliest cancer. 80% of esophageal cancers occur in squamous cells. In Iran, this type of cancer is more prevalent in Golestan province. Before the onset of this type of cancer, histological precursor lesions ...
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The esophageal carcinoma is the eight most predominate malignancy in the world and the sixth deadliest cancer. 80% of esophageal cancers occur in squamous cells. In Iran, this type of cancer is more prevalent in Golestan province. Before the onset of this type of cancer, histological precursor lesions emerge in the epithelial tissue of esophageal mucosa that their progression and penetration into the underlying layers of epithelium lead to cancer. This disease starts from a pre-clinical phase in most patients. In most cases, the disease progresses to the same clinical stage in the absence of appropriate therapeutic interventions. In the literature of this cancer, there is no model for the progression of these lesions (dysplasia) at the mesoscopic level. In this study, by using microscopic images of normal and low-grade dysplasia biopsy samples, we proposed a dynamical model based on the globally coupled logistic maps. The model was designed and its parameters were set based on the assumptions of the esophageal epithelium structure, functionality and using the information about the fractal geometry of this tissue. The model performance was evaluated by computation the pattern of Lyapunov exponent variations across the epithelium thickness. In this model, the decreasing trend of this index for normal tissue had a reasonable accuracy and sensitivity to diagnose it from the low-grade dysplasia. Besides, the model results show that it can be a direct relationship between the structural complexity of this biological system and its timeliness uncertainty.
Ghasem Sadeghi Bajestani; Abbas Monzavi; Seyed Mohammad Reza Hashemi Golpayegani; Farah Ashrafzadeh
Volume 11, Issue 2 , June 2017, , Pages 167-185
Abstract
Autism spectrum disorder (ASD) is a common disorder among children which despite painstakingly effort, it is not yet possible to be precisely detected using paraclinical methods. On the other hand, early detection, before 18th month, has pivotal role in treatment procedure. In this study, we present ...
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Autism spectrum disorder (ASD) is a common disorder among children which despite painstakingly effort, it is not yet possible to be precisely detected using paraclinical methods. On the other hand, early detection, before 18th month, has pivotal role in treatment procedure. In this study, we present a method for early diagnosis of ASD based on the qualitative analysis of the Electroencephalogram (EEG) signal. We develop a new domain for quantifying the quality of interaction is present. We name it 'stretching – folding space’ (SFS). This domain is based on cybernetics, holistic and information-based analysis approaches. Therefore, it provides a non-deterministic approach to the biosignals. We collected data from 60 normal and 60 children with ASD in the range of 3-10 years old. We extracted features from the data in the SFS domain. The design of the study is self-controlled, meaning that each child serves as his/her own control. Each subject in the study watched a cartoon with and without sound, and the EEG signals were recorded. Statistical tests are applied on the extracted qualitative features in the SFS domain. The difference between the features of the data for each group (normal and ASD) was extracted, and the difference were compared between the groups. The results indicate that there is a statistically significant difference between the SFS features of normal and autism children. We conclude that our proposed method can serve as a new signal processing tool for diagnosing autism.
