Aiding Diagnosis of Normal Pressure Hydrocephalus with Enhanced Gait Feature Separability
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Transcript of Aiding Diagnosis of Normal Pressure Hydrocephalus with Enhanced Gait Feature Separability
Aiding Diagnosis of Normal Pressure Hydrocephalus with Enhanced Gait
Feature Separability Shanshan Chen, Adam T. Barth,
Maïté Brandt-Pearce, John Lach
Charles L. Brown Dept. of Electrical & Computer
Engineering
Bradford C. BennettMotion Analysis and Motor Performance
LabDepartment of
Orthopedic Surgery
Jeffery T. Barth , Donna K. Broshek, Jason R. Freeman, Hillary L. Samples
Department of Psychiatry and Neurobehavioral Sciences
Excessive accumulationcerebrospinal fluid(CSF)
Normal Pressure Hydrocephalus(NPH)
2Drains CSF toAbdomen
Surgical Implant
Treatment(Shunting)
Symptoms:Cognitive degradation
Gait DisturbanceUrinary Incontinence
Diagnosis?
Differential Diagnosis in Clinics
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High Volume Lumbar Puncture (HVLP) procedure
Temporarily Drains CSF
Before HVLPBrain imagingCognitive skills assessmentsGait performance
After HVLPCognitive skills assessmentsGait performance
cf.
Current Clinical Gait Evaluation• 10m Walk with Stopwatch Timing
• Step Length• Step Time• Gait Speed• Subjective Observation from Clinicians
• Limitations• Low precision
• Incapable of capturing of subtle gait improvement• Short-term
• Subjected to fluctuations in gait performance• Incapable of capturing gradual gait improvement
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Qualitative Patient Response
5Maximal Response
Gait
Perfo
rman
ce
∆𝑻=?
Longitudinal Timeline (days)
NPH Group
Individual NPH
Other DementiaGroups
HVLPCurrent observation time window
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Confounding!
Platform and Data Collection
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• 6 Suspected NPH Subjects• 4 are diagnosed as NPH, 2 are not
• Inertial Sensor Nodes on Waist, Wrists, Lower Limbs• Validation
• Shunting record and following-up studies
TEMPO 3.1 System 6 DOF motion sensing
a wrist watch form factorDeveloped by the INERTIA Team
• Inertial Body Sensor Networks (BSNs)• Emerging Research on Gait Analysis using Inertial BSNs• Less Invasive and More Wearable
• Potential for continuous longitudinal analysis
Gait Feature Extraction -- Temporal Gait Features• Stride Time Standard Deviation• Average Double Stance Time • Neither Feature Separates the NPH Group and non-NPH Group
7NPH 1NPH 2
NPH 3NPH 4
Non-NPH 1
Non-NPH 2
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Before HVLPAfter HVLP
Average DoubleStance Time (s)
NPH Subject after HVLPHealthy Subject
Gait Feature Extraction-- via Nonlinear Analysis
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• Different Diverging Rates of Different Gaits• Lyapunov Exponent (LyE)
NPH Subject beforeHVLP
NPH 1NPH 2
NPH 3NPH 4
Non-NPH 1
Non-NPH 2
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Before HVLP
After HVLP
Lyapunov Exponent
Results: Nonlinear Gait Feature
9Lyapunov Exponent Gait Stability
Future Work• Larger Size Study• Clinical Interface in Development
• Visualization of the data• Interpretation of the data
• Longer-term Monitoring
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11Maximal Response
Gait
Perfo
rman
ce
Longitudinal Timeline (days)
NPH Group
Individual NPH
Other DementiaGroups
HVLPFuture Observation time window
𝒎𝒂𝒙 ∆
Future Work
Conclusion• Pilot Study
• Real system deployment on real subjects• Advanced Signal Processing with Domain Knowledge
• Identifying and extracting relevant features• Providing separability to aid clinical decision
• Exemplification
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Thanks!