Bioelectrics
Elham Dehghanpur Deharab; Peyvand Ghaderyan
Volume 15, Issue 4 , March 2022, , Pages 279-287
Abstract
Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience ...
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Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience and the skill of the physician. Therefore, the development of an automated, low cost, and reliable diagnostic system is desirable for researchers. In this study, a handwriting signal including cognitive and motor-perceptual components has been used as a non-invasive, cost effective and reliable characteristic in identifying PD-related cognitive and motor dysfunctions. For this purpose, the matching pursuit algorithm with high time-frequency resolution has been employed to decompose X-Y coordinates. It provides a sparse representation of the handwriting signals and quantifies the basic information about the local changes in the handwriting signals. The proposed method is evaluated on a database with 31 healthy samples and 29 Parkinson's samples using the support vector machine classifier and obtained results yields an average accuracy rate of 90%, sensitivity rate of 91.59% and specificity rate of 90%. Comparing different writing tasks has also demonstrated superior performance of writing an entire sentence for PD detection.
Bioelectrics
Farzaneh Manzari; Peyvand Ghaderyan
Volume 15, Issue 4 , March 2022, , Pages 313-328
Abstract
Obsessive-Compulsive Disorder (OCD) is the fourth most common mental disorder and the tenth cause of disability worldwide. This disorder can lead to cognitive impariments in attention, memory, thinking, auditory processing of words and visual cognition. Previous studies have demonstrated that OCD is ...
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Obsessive-Compulsive Disorder (OCD) is the fourth most common mental disorder and the tenth cause of disability worldwide. This disorder can lead to cognitive impariments in attention, memory, thinking, auditory processing of words and visual cognition. Previous studies have demonstrated that OCD is associated with changes in connectivity between different lobes of the brain. Hence, the quantification of symmetry and connectivity between different brain regions has attracted great attention. This study has provided a new efficient approach based on analytic representation of EEG signals and statistical features to quantify the difference of intrinsic components of brain activity between brain lobes. For this purpose, phase spectra and amplitude envelopes of the analytic EEG signals have been extracted and analyzed. Furthermore, Non-Negative Least Square sparse classification method has been used for discriminating between healthy control group and OCD patients. The detection capability of the proposed method has been studied in 19 healthy subjects and 11 patients, performing simple flanker tasks. The obtained results have demonstrated the effectiveness of the combined amplitude and phase information in OCD detection with high average accuracy rate of 93.78 %. In comparison between different regions, the inter-hemispheric features and those extracted from the frontal lobe and frontal-parietal network have shown more efficiency in diagnosing the OCD. This study has also highlighted more importance of amplitude information in the OCD detection.
Gisoo Fathi; Peyvand Ghaderyan
Volume 15, Issue 2 , August 2021, , Pages 161-174
Abstract
Parkinson’s Disease (PD) is one of the most common neurodegenerative diseases that cause abnormal gait patterns by affecting central nervous system. Since this disease is incurable, the reliable diagnosis can lead to slowing disease progression, reducing the risk of physical injuries and improving ...
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Parkinson’s Disease (PD) is one of the most common neurodegenerative diseases that cause abnormal gait patterns by affecting central nervous system. Since this disease is incurable, the reliable diagnosis can lead to slowing disease progression, reducing the risk of physical injuries and improving the quality of patient's life. In this regard, the development of fast, cost-effective and reliable detection systems is essential. This study has therefore proposed a detection method using vertical ground reaction force signals, which provide a non-invasive and useful index of the motor control function. It is based on generalized singular value decomposition, K-Nearest Neighbor (KNN) and Probabilistic Neural Network (PNN). The performance of the algorithm has been evaluated by gait signal of 93 individuals with PD and 73 healthy controls. The results have demonstrated that the proposed new symmetric feature is able to achieve 96.19% and 95.67% accuracy rates, 97.22% and 93.35% sensitivity rates, 95.02% and 97.33% specificity rates using the KNN and PNN classifiers, respectively. Furthermore, average accuracy rates of 98.23% and 98.51%, sensitivity rates of 93.5% and 100%, specificity rates of 100% and 96.53% have been obtained for stage classification using these two classifiers. The obtained high average accuracy rates have confirmed the promising capability of the proposed non-invasive and cost-effective method in PD detection and stage classification, which makes it suitable for clinical applications.
Masume Saljuqi; Peyvand Ghaderyan
Volume 15, Issue 1 , May 2021, , Pages 59-71
Abstract
In the recent years, the diagnosis of Neurodegenerative Diseases (NDDs) has been one of the most challenging problems in the medical fields. Amyotrophic Lateral Sclerosis (ALS), Parkinson's Disease (PD) and Huntington's Disease (HD) are a group of neurological disorders affecting the quality of patient’s ...
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In the recent years, the diagnosis of Neurodegenerative Diseases (NDDs) has been one of the most challenging problems in the medical fields. Amyotrophic Lateral Sclerosis (ALS), Parkinson's Disease (PD) and Huntington's Disease (HD) are a group of neurological disorders affecting the quality of patient’s life. Occurrence of these diseases is due to the deterioration of motor neurons, causing human gait disturbance and asymmetry between the right and left limbs. For this purpose, in this paper various gait signals namely stride, swing, and stance intervals (from both legs) have been decomposed using a Matching Pursuit (MP) algorithm. Then, two sets of differential and dynamic features have been extracted from the MP coefficients in order to quantify the amount of divergence between both limbs. Finally, the principal components of these features have been fed as an input to sparse Non-Negative Least Squares (NNLS) classifier. The proposed algorithm has been evaluated using the gait signals of 16 healthy control subjects, 13 patients with Amyotrophic Lateral Sclerosis (ALS), 15 patients with Parkinson’s Disease (PD) and 20 patients with Huntington’s Disease (HD). The results showed that the proposed method has achieved high average accuracy rates of 84.10%, 86.67%, and 91.43% for ALS, PD, and HD detection, respectively.
