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Hye Mi Lee 4 Articles
Clinical Characteristics of Involuntary Movement in Hospitalized Patients
Kyum-Yil Kwon, Hye Mi Lee, Seon-Min Lee, Seong-Beom Koh
J Mov Disord. 2019;12(1):31-36.   Published online December 20, 2018
  • 7,259 View
  • 207 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDF
Neurological symptoms in hospitalized patients are not rare, and neurological consultation for movement disorders is especially important in evaluating or managing those with various movement disorders. Therefore, we investigated a clinical pattern of in-hospital consultations for various movement disorders in a tertiary care university hospital.
Over two years, a total of 202 patients (70.7 ± 11.8 years of age) presenting with movement disorders referred to movement disorder specialists were investigated.
The main symptoms referred by nonneurologists were tremor (56.9%), parkinsonism (16.8%), and gait disturbance (8.9%). The most frequent diagnostic category was toxic/metabolic-caused movement disorder (T/MCMD) (35%) with regard to medications, followed by Parkinson’s disease (PD) (16%). Regarding the mode of onset, T/MCMD was the leading cause for acute (68%) and subacute onset (46%), while PD was the leading disorder (31%) for chronic onset.
The current study showed a characteristic pattern of inpatients presenting with movement disorders. Furthermore, our findings highlighted the clinical significance of drug use or metabolic problems for treating this patient population.


Citations to this article as recorded by  
  • Pattern and frequency of involuntary movements: hospital-based study
    H. N. El Tallawy, A. S. Shalash, M. A. Abdelhamed, R. E. Elsabrout
    The Egyptian Journal of Neurology, Psychiatry and Neurosurgery.2023;[Epub]     CrossRef
  • Clinical Spectrum of Movement Disorders in Neurology Inpatients in a Tertiary Care Centre
    Shabeer Ahmad Paul, Gouranga Prasad Mondal, Ramesh Bhattacharyya, Kartik Chandra Ghosh, Sarbajit Das, Suman Das, Hema Krishna, Chandrakanta Patra
    Journal of Neurosciences in Rural Practice.2021; 12: 581.     CrossRef
Quantitative Gait Analysis in Patients with Huntington’s Disease
Seon Jong Pyo, Hanjun Kim, Il Soo Kim, Young-Min Park, Mi-Jung Kim, Hye Mi Lee, Seong-Beom Koh
J Mov Disord. 2017;10(3):140-144.   Published online August 31, 2017
  • 7,551 View
  • 141 Download
  • 19 Web of Science
  • 19 Crossref
AbstractAbstract PDF
Gait disturbance is the main factor contributing to a negative impact on quality of life in patients with Huntington’s disease (HD). Understanding gait features in patients with HD is essential for planning a successful gait strategy. The aim of this study was to investigate temporospatial gait parameters in patients with HD compared with healthy controls.
We investigated 7 patients with HD. Diagnosis was confirmed by genetic analysis, and patients were evaluated with the Unified Huntington’s Disease Rating Scale (UHDRS). Gait features were assessed with a gait analyzer. We compared the results of patients with HD to those of 7 age- and sex-matched normal controls.
Step length and stride length were decreased and base of support was increased in the HD group compared to the control group. In addition, coefficients of variability for step and stride length were increased in the HD group. The HD group showed slower walking velocity, an increased stance/swing phase in the gait cycle and a decreased proportion of single support time compared to the control group. Cadence did not differ significantly between groups. Among the UHDRS subscores, total motor score and total behavior score were positively correlated with step length, and total behavior score was positively correlated with walking velocity in patients with HD.
Increased variability in step and stride length, slower walking velocity, increased stance phase, and decreased swing phase and single support time with preserved cadence suggest that HD gait patterns are slow, ataxic and ineffective. This study suggests that quantitative gait analysis is needed to assess gait problems in HD.


Citations to this article as recorded by  
  • Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A Survey
    Samaneh Zolfaghari, Sumaiya Suravee, Daniele Riboni, Kristina Yordanova
    ACM Computing Surveys.2024; 56(1): 1.     CrossRef
  • The semicircular canal function is preserved with little impact on falls in patients with mild Parkinson's disease
    Jun-Pyo Hong, Hanim Kwon, Euyhyun Park, Sun-Uk Lee, Chan-Nyoung Lee, Byung-Jo Kim, Ji-Soo Kim, Kun-Woo Park
    Parkinsonism & Related Disorders.2024; 118: 105933.     CrossRef
  • Gene-dosage- and sex-dependent differences in the prodromal-Like phase of the F344tgHD rat model for Huntington disease
    Veronika Ratz-Wirsching, Johanna Habermeyer, Sandra Moceri, Julia Harrer, Christoph Schmitz, Stephan von Hörsten
    Frontiers in Neuroscience.2024;[Epub]     CrossRef
  • Vision-Based Motion Capture for the Gait Analysis of Neurodegenerative Diseases: A Review
    David Vun Sing Yee, Robert Bowers, Anthony McGarry
    Gait & Posture.2024;[Epub]     CrossRef
  • Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review
    Huan Zhao, Junyi Cao, Junxiao Xie, Wei-Hsin Liao, Yaguo Lei, Hongmei Cao, Qiumin Qu, Chris Bowen
    DIGITAL HEALTH.2023; 9: 205520762311735.     CrossRef
  • Spatiotemporal Gait Parameters in Adults With Premanifest and Manifest Huntington’s Disease: A Systematic Review
    Sasha Browning, Stephanie Holland, Ian Wellwood, Belinda Bilney
    Journal of Movement Disorders.2023; 16(3): 307.     CrossRef
  • Walking pattern analysis using GAIT cycles and silhouettes for clinical applications
    S.M.H. Sithi Shameem Fathima, K.A. Jyotsna, Thiruveedula Srinivasulu, Kande Archana, M. Tulasi rama, S. Ravichand
    Measurement: Sensors.2023; 30: 100893.     CrossRef
  • Human Gait Analysis in Neurodegenerative Diseases: A Review
    Grazia Cicirelli, Donato Impedovo, Vincenzo Dentamaro, Roberto Marani, Giuseppe Pirlo, Tiziana R. D'Orazio
    IEEE Journal of Biomedical and Health Informatics.2022; 26(1): 229.     CrossRef
  • Development of Neuro-Degenerative Diseases’ Gait Classification Algorithm Using Convolutional Neural Network and Wavelet Coherence Spectrogram of Gait Synchronization
    Febryan Setiawan, An-Bang Liu, Che-Wei Lin
    IEEE Access.2022; 10: 38137.     CrossRef
  • Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
    Alexandra-Maria Tăuţan, Bogdan Ionescu, Emiliano Santarnecchi
    Artificial Intelligence in Medicine.2021; 117: 102081.     CrossRef
  • Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features
    Febryan Setiawan, Che-Wei Lin
    Brain Sciences.2021; 11(7): 902.     CrossRef
  • The effects of dual-task cognitive interference on gait and turning in Huntington’s disease
    Nicollette L. Purcell, Jennifer G. Goldman, Bichun Ouyang, Yuanqing Liu, Bryan Bernard, Joan A. O’Keefe, Pedro Gonzalez-Alegre
    PLOS ONE.2020; 15(1): e0226827.     CrossRef
  • Gait variability as digital biomarker of disease severity in Huntington’s disease
    Heiko Gaßner, Dennis Jensen, F. Marxreiter, Anja Kletsch, Stefan Bohlen, Robin Schubert, Lisa M. Muratori, Bjoern Eskofier, Jochen Klucken, Jürgen Winkler, Ralf Reilmann, Zacharias Kohl
    Journal of Neurology.2020; 267(6): 1594.     CrossRef
  • Evaluation of Vertical Ground Reaction Forces Pattern Visualization in Neurodegenerative Diseases Identification Using Deep Learning and Recurrence Plot Image Feature Extraction
    Che-Wei Lin, Tzu-Chien Wen, Febryan Setiawan
    Sensors.2020; 20(14): 3857.     CrossRef
  • Cerebral dopamine neurotrophic factor (CDNF) protects against quinolinic acid-induced toxicity in in vitro and in vivo models of Huntington’s disease
    P. Stepanova, V. Srinivasan, D. Lindholm, M. H. Voutilainen
    Scientific Reports.2020;[Epub]     CrossRef
  • Rapid and robust patterns of spontaneous locomotor deficits in mouse models of Huntington’s disease
    Taneli Heikkinen, Timo Bragge, Niina Bhattarai, Teija Parkkari, Jukka Puoliväli, Outi Kontkanen, Patrick Sweeney, Larry C. Park, Ignacio Munoz-Sanjuan, Yuqing Li
    PLOS ONE.2020; 15(12): e0243052.     CrossRef
  • Rule based classification of neurodegenerative diseases using data driven gait features
    Kartikay Gupta, Aayushi Khajuria, Niladri Chatterjee, Pradeep Joshi, Deepak Joshi
    Health and Technology.2019; 9(4): 547.     CrossRef
  • Discovery of Arginine Ethyl Ester as Polyglutamine Aggregation Inhibitor: Conformational Transitioning of Huntingtin N-Terminus Augments Aggregation Suppression
    Virender Singh, Kinjal A. Patel, Raj Kumar Sharma, Pratik R. Patil, Abhayraj S. Joshi, Rashmi Parihar, Thamarailingam Athilingam, Neeraj Sinha, Subramaniam Ganesh, Pradip Sinha, Ipsita Roy, Ashwani Kumar Thakur
    ACS Chemical Neuroscience.2019; 10(9): 3969.     CrossRef
  • Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study
    Eddy Sánchez-DelaCruz, Roberto Weber, R. R. Biswal, Jose Mejía, Gandhi Hernández-Chan, Heberto Gómez-Pozos
    Computational and Mathematical Methods in Medicine.2019; 2019: 1.     CrossRef
Cognition and Visit-to-Visit Variability of Blood Pressure and Heart Rate in De Novo Patients with Parkinson’s Disease
Kyum-Yil Kwon, Seon Jong Pyo, Hye Mi Lee, Woo-Keun Seo, Seong-Beom Koh
J Mov Disord. 2016;9(3):144-151.   Published online September 21, 2016
  • 13,654 View
  • 123 Download
  • 8 Web of Science
  • 8 Crossref
AbstractAbstract PDFSupplementary Material
We sought to identify whether the characteristics of long-term visit-to-visit blood pressure (BP) and heart rate (HR) are related to baseline cognitive profiles in, Parkinson’s disease (PD).
We selected drug-naïve PD patients who visited our hospital at least 10 times with a baseline assessment of the Seoul neuropsychological battery. BP and HR were measured at each visit, and the variability of the systolic BP/diastolic BP (DBP) and HR was derived from the parameters of serial 10 office visits. Mild cognitive impairment (MCI) in PD patients was determined according to the proposed criteria with a cut-off value of z-score ≤ -2.
Forty-seven patients with PD (mean follow-up duration = 22.3 months) were enrolled in the study. Compared with non-MCI PD patients, MCI PD patients revealed a significant increase in HR and/or variability in DBP.
This exploratory study showed that baseline cognition in drug-naïve PD patients might be related to the visit-to-visit variability of DBP and/or HR.


Citations to this article as recorded by  
  • Association between the blood pressure variability and cognitive decline in Parkinson's disease
    Yi Xiao, Tianmi Yang, Lingyu Zhang, Qianqian Wei, Ruwei Ou, Yanbing Hou, Kuncheng Liu, Junyu Lin, Qirui Jiang, Huifang Shang
    Brain and Behavior.2023;[Epub]     CrossRef
  • Cardiovascular autonomic dysfunction is associated with executive dysfunction and poorer quality of life in progressive supranuclear palsy-Richardson’s syndrome
    Peng Liu, Yueting Chen, Bo Wang, Sheng Wu, Leilei Zeng, Zhidong Cen, Dehao Yang, Haotian Wang, Xinhui Chen, Lebo Wang, Zhiyuan Ouyang, Wei Luo
    Journal of Clinical Neuroscience.2022; 96: 147.     CrossRef
  • Blood Pressure Variability and Cognitive Function: a Scoping Review
    Nur Fazidah Asmuje, Sumaiyah Mat, Phyo Kyaw Myint, Maw Pin Tan
    Current Hypertension Reports.2022; 24(10): 375.     CrossRef
  • Associations of cognitive dysfunction with motor and non-motor symptoms in patients with de novo Parkinson’s disease
    Kyum-Yil Kwon, Suyeon Park, Rae On Kim, Eun Ji Lee, Mina Lee
    Scientific Reports.2022;[Epub]     CrossRef
  • Blood pressure variability is related to faster cognitive decline in ischemic stroke patients: PICASSO subanalysis
    Yerim Kim, Jae-Sung Lim, Mi Sun Oh, Kyung-Ho Yu, Ji Sung Lee, Jong-Ho Park, Yong-Jae Kim, Joung-Ho Rha, Yang-Ha Hwang, Sung Hyuk Heo, Seong Hwan Ahn, Ju-Hun Lee, Sun U. Kwon
    Scientific Reports.2021;[Epub]     CrossRef
  • The correlation of blood pressure variability and cognitive function in hypertension patients: A meta‐analysis
    Xiaojie Jin, Yi Lu, Peng Zhao
    International Journal of Clinical Practice.2021;[Epub]     CrossRef
  • Burden and correlates of cognitive impairment among hypertensive patients in Tanzania: a cross-sectional study
    Pedro Pallangyo, Zabella S. Mkojera, Makrina Komba, Lucy R. Mgopa, Smita Bhalia, Henry Mayala, Salma Wibonela, Nsajigwa Misidai, Happiness J. Swai, Jalack Millinga, Ester Chavala, Peter R. Kisenge, Mohamed Janabi
    BMC Neurology.2021;[Epub]     CrossRef
  • Backward Gait is Associated with Motor Symptoms and Fear of Falling in Patients withDe NovoParkinson's Disease
    Kyum-Yil Kwon, Suyeon Park, Hye Mi Lee, Young-Min Park, Jinhee Kim, Jaehwan Kim, Seong-Beom Koh
    Journal of Clinical Neurology.2019; 15(4): 473.     CrossRef
Many Faces of Parkinson’s Disease: Non-Motor Symptoms of Parkinson’s Disease
Hye Mi Lee, Seong-Beom Koh
J Mov Disord. 2015;8(2):92-97.   Published online May 31, 2015
  • 22,847 View
  • 548 Download
  • 101 Web of Science
  • 99 Crossref
AbstractAbstract PDF
Parkinson’s disease (PD) is a multi-systemic disorder that is characterized by a combination of motor and non-motor symptoms (NMS). The dopaminergic neurodegeneration of PD is involved in the genesis of NMS, but other conditions and side effects of levodopa are also associated with NMS. NMS can develop at all stage of PD and rapid eyeball movement sleep behavior disorder (RBD), constipation, depression, and olfactory dysfunction are considered prodromal signs of PD. Many NMS related with motor deficits and cognitive dysfunction. Some NMS including olfactory dysfunction, RBD and abnormal stereopsis are associated with presence of other NMS of PD. In addition, several NMS can be helpful to differentiate between idiopathic PD and other parkinsonian disorders. Early recognition and management of NMS in PD patients is important for preserving quality of life.


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    European Journal of Ophthalmology.2024; 34(1): 252.     CrossRef
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