David A Peterson1, Gwen C Littlewort2, Marian S Bartlett2, Antonella Macerollo2, Joel S Perlmutter2, H A Jinnah2, Mark Hallett2, Terrence J Sejnowski2. 1. From the Computational Neurobiology Laboratory (D.A.P., T.J.S.) and Howard Hughes Medical Institute (T.J.S.), Salk Institute for Biological Studies; Institute for Neural Computation (D.A.P., G.C.L., M.S.B., T.J.S.), Kavli Institute for Brain and Mind (D.A.P., T.J.S.), Machine Perception Laboratory (G.C.L., M.S.B.), and Division of Biological Sciences (T.J.S.), University of California, San Diego, La Jolla; Sobell Department of Motor Neuroscience and Movement Disorders (A.M.), National Hospital of Neurology and Neurosurgery, Institute of Neurology, University College London, UK; Departments of Neurology, Radiology, and Anatomy and Neurobiology, and Programs in Physical Therapy and Occupational Therapy (J.S.P.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology, Human Genetics, and Pediatrics (H.A.J.), Emory University, Atlanta, GA; and Human Motor Control Section (M.H.), National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD. dap@salk.edu. 2. From the Computational Neurobiology Laboratory (D.A.P., T.J.S.) and Howard Hughes Medical Institute (T.J.S.), Salk Institute for Biological Studies; Institute for Neural Computation (D.A.P., G.C.L., M.S.B., T.J.S.), Kavli Institute for Brain and Mind (D.A.P., T.J.S.), Machine Perception Laboratory (G.C.L., M.S.B.), and Division of Biological Sciences (T.J.S.), University of California, San Diego, La Jolla; Sobell Department of Motor Neuroscience and Movement Disorders (A.M.), National Hospital of Neurology and Neurosurgery, Institute of Neurology, University College London, UK; Departments of Neurology, Radiology, and Anatomy and Neurobiology, and Programs in Physical Therapy and Occupational Therapy (J.S.P.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology, Human Genetics, and Pediatrics (H.A.J.), Emory University, Atlanta, GA; and Human Motor Control Section (M.H.), National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD.
Abstract
OBJECTIVE: To compare clinical rating scales of blepharospasm severity with involuntary eye closures measured automatically from patient videos with contemporary facial expression software. METHODS: We evaluated video recordings of a standardized clinical examination from 50 patients with blepharospasm in the Dystonia Coalition's Natural History and Biorepository study. Eye closures were measured on a frame-by-frame basis with software known as the Computer Expression Recognition Toolbox (CERT). The proportion of eye closure time was compared with 3 commonly used clinical rating scales: the Burke-Fahn-Marsden Dystonia Rating Scale, Global Dystonia Rating Scale, and Jankovic Rating Scale. RESULTS: CERT was reliably able to find the face, and its eye closure measure was correlated with all of the clinical severity ratings (Spearman ρ = 0.56, 0.52, and 0.56 for the Burke-Fahn-Marsden Dystonia Rating Scale, Global Dystonia Rating Scale, and Jankovic Rating Scale, respectively, all p < 0.0001). CONCLUSIONS: The results demonstrate that CERT has convergent validity with conventional clinical rating scales and can be used with video recordings to measure blepharospasm symptom severity automatically and objectively. Unlike EMG and kinematics, CERT requires only conventional video recordings and can therefore be more easily adopted for use in the clinic.
OBJECTIVE: To compare clinical rating scales of blepharospasm severity with involuntary eye closures measured automatically from patient videos with contemporary facial expression software. METHODS: We evaluated video recordings of a standardized clinical examination from 50 patients with blepharospasm in the Dystonia Coalition's Natural History and Biorepository study. Eye closures were measured on a frame-by-frame basis with software known as the Computer Expression Recognition Toolbox (CERT). The proportion of eye closure time was compared with 3 commonly used clinical rating scales: the Burke-Fahn-Marsden Dystonia Rating Scale, Global Dystonia Rating Scale, and Jankovic Rating Scale. RESULTS: CERT was reliably able to find the face, and its eye closure measure was correlated with all of the clinical severity ratings (Spearman ρ = 0.56, 0.52, and 0.56 for the Burke-Fahn-Marsden Dystonia Rating Scale, Global Dystonia Rating Scale, and Jankovic Rating Scale, respectively, all p < 0.0001). CONCLUSIONS: The results demonstrate that CERT has convergent validity with conventional clinical rating scales and can be used with video recordings to measure blepharospasm symptom severity automatically and objectively. Unlike EMG and kinematics, CERT requires only conventional video recordings and can therefore be more easily adopted for use in the clinic.
Authors: Giovanni Defazio; Mark Hallett; Hyder A Jinnah; Glenn T Stebbins; Angelo F Gigante; Gina Ferrazzano; Antonella Conte; Giovanni Fabbrini; Alfredo Berardelli Journal: Mov Disord Date: 2015-04 Impact factor: 10.338
Authors: Ling Yan; Matt Hicks; Korey Winslow; Cynthia Comella; Christy Ludlow; H A Jinnah; Ami R Rosen; Laura Wright; Wendy R Galpern; Joel S Perlmutter Journal: Parkinsonism Relat Disord Date: 2015-01-20 Impact factor: 4.891
Authors: Zheng Zhang; Elizabeth Cisneros; Ha Yeon Lee; Jeanne P Vu; Qiyu Chen; Casey N Benadof; Jacob Whitehill; Ryin Rouzbehani; Dominique T Sy; Jeannie S Huang; Terrence J Sejnowski; Joseph Jankovic; Stewart Factor; Christopher G Goetz; Richard L Barbano; Joel S Perlmutter; Hyder A Jinnah; Brian D Berman; Sarah Pirio Richardson; Glenn T Stebbins; Cynthia L Comella; David A Peterson Journal: Ann Clin Transl Neurol Date: 2022-03-25 Impact factor: 5.430