Browsing by Author "Chen, Shin-Yuan"

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A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
(2012-01-01)
Chen, You-Yin
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Cho, Chien-Wen
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Lin, Sheng-Huang
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Lai, Hsin-Yi
;
Lo, Yu-Chun
Parkinson's Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson's Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality. (C) 2011 Elsevier Ltd. All rights reserved.
Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis
(2011-06-01)
Lai, Hsin-Yi
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Chen, You-Yin
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Lin, Sheng-Huang
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Lo, Yu-Chun
;
Tsang, Siny
Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.
Computing stimulation voltage in a bipolar electrode configuration to avoid side effects during deep brain stimulation
(2015-09-01)
Chuang, Wei-Yi
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Chao, Paul C. -P.
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Chen, Shin-Yuan
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Tsai, Sheng-Tzung
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Young, Kuu-Young
Deep brain stimulation (DBS) in the subthalamic nucleus (STN) has been applied for advanced Parkinson\'s disease (PD). In clinical practices, monopolar configuration is often utilized based on clinical efficiency. Meanwhile, the volume of tissue activated (VTA) by DBS is estimated for controlling and/or limiting the stimulated region. However, side effects like dyskinesia and tremor were accompanied during stimulation. Most studies had found that bipolar stimulation is an alternative approach for side effects avoidance. The goal of this paper is to develop a quantified relationship of stimulation voltage from monopolar to bipolar configuration to improve neural stimulation. In this paper, an electromagnetic finite element model is first built for a patient-specific physiological brain model, which is established by magnetic resonance imaging (MRI) data. The model is then used for finite element analysis (PEA) to estimate VTA with varied electrode voltage in monopolar and bipolar configurations. With the goals to avoid side effects and achieve symptoms suppression, the stimulation voltage of bipolar configuration is successfully computerized based on the electromagnetic PEA simulations. Experiments are conducted successfully to validate simulation results. (C) 2015 Elsevier B.V. All rights reserved.
Dorsolateral Subthalamic Neuronal Activities Enhanced By Median Nerve Stimulation Facilitate Parkinson\'S Disease During Deep Brain Stimulation In General Anesthesia
(2013-01-01)
Tsai, Sheng-Tzung
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Chung, Wei-Yi
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Kuo, Chung-Chih
;
Chen, Shin-Yuan
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Dorsolateral subthalamic neuronal activity enhanced by median nerve stimulation characterizes Parkinson\'s disease during deep brain stimulation with general anesthesia
(2015-12-01)
Tsai, Sheng-Tzung
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Chuang, Wei-Yi
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Kuo, Chung-Chih
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Chao, Paul C. P.
;
Chen, Tsung-Ying
OBJECT Deep brain stimulation (DBS) surgery under general anesthesia is an alternative option for patients with Parkinson\'s-disease (PD). However, few studies are available that report whether neuronal firing can be accurately recorded during this condition. In this study the authors attempted to characterize the neuronal activity of the subthalamic nucleus (STN) and elucidate the influence of general anesthetics on neurons during DBS surgery in patients with PD. The benefit of median nerve stimulation (MNS) for localization of the dorsolateral subterritory of the STN, which is involved in sensorimotor function, was explored. METHODS Eight patients with PD were anesthetized with desflurane and underwent contralateral MNS at the wrist during microelectrode recording of the STN. The authors analyzed the spiking patterns and power spectral density (PSD) of the background activity along each penetration track and determined the spatial correlation to the target location, estimated using standard neurophysiological procedures. RESULTS The dorsolateral STN spiking pattern showed a more prominent bursting pattern without MNS and more oscillation with MNS. In terms of the neural oscillation of the background activity, beta-band oscillation dominated within the sensorimotor STN and showed significantly more PSD during MNS (rho < 0.05). CONCLUSIONS Neuronal firing within the STN could be accurately identified and differentiated when patients with PD received general anesthetics. Median nerve stimulation can enhance the neural activity in beta-band oscillations, which can be used as an index to ensure optimal electrode placement via successfully tracked dorsolateral STN topography.
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
(2011-11-10)
Chen, Shih-Wei
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Lin, Sheng-Huang
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Liao, Lun-De
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Lai, Hsin-Yi
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Pei, Yu-Cheng
Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.