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3. Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients

Author

Guillermina Vivar, Dora-Luz Almanza-Ojeda, Irene Cheng, Juan Carlos Gomez, J. A. Andrade-Lucio, and Mario-Alberto Ibarra-Manzano

Subjects
tremor, Parkinson’s disease, SDH method, classification, Chemical technology, TP1-1185
Abstract

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson’s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson’s disease by the Unified Parkinson’s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.

Published
2019
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6. Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients

Author

Mario Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Guillermina Vivar, Irene Cheng, Juan Carlos Gomez, and Jose A. Andrade-Lucio

Subjects
Male, Parkinson's disease, Computer science, Early detection, SDH method, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Severity of Illness Index, Article, Analytical Chemistry, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, Monitoring, Physiologic, business.industry, Homogeneity (statistics), Pattern recognition, Parkinson Disease, medicine.disease, tremor, Atomic and Molecular Physics, and Optics, nervous system diseases, classification, Quality of Life, Parkinson’s disease, 020201 artificial intelligence & image processing, Female, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson&rsquo, s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson&rsquo, s disease by the Unified Parkinson&rsquo, s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands, the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size, however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.

Published
2019

7. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

Author

Carlos R. Montoro-Sanjose, Mario Alberto Ibarra-Manzano, Horacio Rostro-Gonzalez, Rene de Jesus Romero-Troncoso, Dora-Luz Almanza-Ojeda, Patricia Batres-Mendoza, and Erick Israel Guerra-Hernandez

Subjects
Support Vector Machine, Computer science, Movement, Speech recognition, 02 engineering and technology, Electroencephalography, lcsh:Chemical technology, Biochemistry, Article, Analytical Chemistry, Cognition, Motor imagery, motor imagery, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, lcsh:TP1-1185, Time domain, electroencephalography (EEG), Electrical and Electronic Engineering, Quaternion, Instrumentation, Brain Mapping, Signal processing, quaternion-based signal analysis (QSA), medicine.diagnostic_test, business.industry, 020208 electrical & electronic engineering, Brain, brain-computer interface (BCI), Pattern recognition, Atomic and Molecular Physics, and Optics, Support vector machine, Brain-Computer Interfaces, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

Published
2016

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