Normal Pressure Hydrocephalus – an Aging Condition

NPH is a neuropathy caused by abnormal accumulation of cerebrospinal fluid (CSF) that normally surrounds the brain . The symptoms are usually described as a classic triad of gait disturbance, dementia or mental decline and urinary incontinence . To diagnose NPH, a high volume lumbar puncture (HVLP) procedure to remove excess fluid is usually given the suspected NPH subjects followed by the evaluation of clinical response to CSF removal. If diagnosed successfully as NPH, patients will be treated with an invasive, long term intervention – the ventriculo-peritoneal (VP) cerebral shunt to drain excess CSF to the abdomen where it is absorbed. However, risks associated with a cerebral shunt include intracranial hematoma, cerebral edema, crushed brain tissue and herniation, revealing the importance of an accurate diagnosis. Medical literature has suggested that improvement in gait pre- to post-HVLP is often a good marker of diagnosis in the decision to proceed with shunt surgery.  However, such gait improvement is usually based on clinical observation rather than objective, quantitative gait analysis, and even assessments performed with modern gait laboratory equipment only provide snapshots of the patient’s gait. This is especially problematic given that patients have variable HVLP response times, so longer continuous gait monitoring is necessary to provide high confidence NPH diagnosis.

This project seeks to evaluate the TEMPO system as an NPH diagnostic tool. As a inertial BSN system, TEMPO provides continuous and non-invasive gait data collection in any location over an extended period of time. Through an in-clinic human subjects pilot study (IRB approval has already been obtained), this project will lay the groundwork and provide preliminary data for a follow up study (and corresponding NIH proposal) in which TEMPO systems are deployed for gait data collection over the two days pre- and post-HVLP. This project will serve to evaluate continuous gait assessment usingTEMPO and personalized signal processing techniques as a tool for improving physicians’ abilities to diagnose – and ultimately treat – NPH without the tremendous cost and inconvenience of inpatient monitoring or multiple outpatient visits, and with greater safety and comfort to an aging, stress-vulnerable, and growing population.

Research Plan

The proposed project is a one year pilot study for a subsequent multi-year study (and corresponding NIH R01 proposal) to fully evaluate the efficacy of continuous, non-invasive gait assessment for NPH diagnosis through multi-day data collections pre- and post-HPLV. Towards the preparation for that study, we propose the following research activities over the following year:

1.    Perform in-clinic TEMPO data collections on NPH patients (five nodes – both ankles, both wrists, and sacrum) during the pre- and post-HVLP gait assessments that are currently performed. TEMPO data collections will also be performed in VICON motion capture laboratories, which are the gold-standard for gait analysis. These collections will help inform, validate, and evaluate the use of TEMPO in NPH gait assessment.

2.    Develop a signal processing plan and perform a series of longitudinal data collection sessions on each patient to measure intra-individual gait variations (e.g., differences pre- and post-HVLP). The type of learning machine and data filtering process chosen will depend on both the nature of the information sought by the clinicians and the computational capabilities of TEMPO. We will consider a simple neural network, a support vector machine, the CMAX, as well as a myriad of feature extraction procedures (principal component analysis, information theoretic entropy maximizing techniques, wavelet decomposition, etc.). Both data compression and classification will be addressed. Performance in terms of classification error and loss of signal fidelity will be compared to similar algorithms trained across individuals and to more powerful techniques developed for non-resource-constrained machines.

 Future Work:

Prepare TEMPO for the multi-day data collections to be performed in the second year of the study. In particular, when TEMPO is operating at the highest data rate with all sensors active, the battery supply will be exhausted in approximately four hours. While this is sufficient for the short, in-clinic data collection sessions that will be performed in this pilot phase, the following steps will be taken to achieve the battery life extension necessary for the follow study:

A.   The longer-term data collections do not require real-time analysis. Instead, data can be processed on-node and the essential information downloaded from the system when the subjects return to the clinic. Therefore, no wireless transmission is necessary, which is the largest consumer of power in the TEMPO system. Writing to non-volatile flash memory consumes significantly less energy per bit than radio frequency transmissions. The TEMPO nodes will therefore be adapted accordingly – replacing radio transceivers with flash memory modules.

B.   We will explore how data can be compressed on-node without compromising the subsequent gait assessment fidelity. Compression can greatly reduce the number of bits that must be written to flash, saving both energy and memory capacity.

C.   Given that on-node signal processing can be used to easily determine if the wearer is walking, the system can enter a low power mode when possible, including stopping flash writes altogether and turning off the gyroscopes (the second largest power consumers in TEMPO). The microcontroller need only occasionally and briefly re-enter active mode and turn on the accelerometers to determine if the subject is walking and, if so, activate the rest of the system. This enables extended operation at well below 1 mW. (paper)

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