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

3

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

4

Qualitative Patient Response

5Maximal Response

Gait

Perfo

rman

ce

∆𝑻=?

Longitudinal Timeline (days)

NPH Group

Individual NPH

Other DementiaGroups

HVLPCurrent observation time window

d

Confounding!

Platform and Data Collection

6

• 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

0

0.1

0.2

0.3

0.4

0.5

0.6

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

0

1

2

3

4

5

6

7

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

10

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

12

Thanks!