EVestG: responses in depressed patients

Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias

Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.

Development of an ultra low noise, miniature signal conditioning device for vestibular evoked response recordings

BACKGROUND

Inner ear evoked potentials are small amplitude (<1 μVpk) signals that require a low noise signal acquisition protocol for successful extraction; an existing such technique is Electrocochleography (ECOG). A novel variant of ECOG called Electrovestibulography (EVestG) is currently investigated by our group, which captures vestibular responses to a whole body tilt. The objective is to design and implement a bio-signal amplifier optimized for ECOG and EVestG, which will be superior in noise performance compared to low noise, general purpose devices available commercially.

METHOD

A high gain configuration is required (>85 dB) for such small signal recordings; thus, background power line interference (PLI) can have adverse effects. Active electrode shielding and driven-right-leg circuitry optimized for EVestG/ECOG recordings were investigated for PLI suppression. A parallel pre-amplifier design approach was investigated to realize low voltage, and current noise figures for the bio-signal amplifier.

RESULTS

In comparison to the currently used device, PLI is significantly suppressed by the designed prototype (by >20 dB in specific test scenarios), and the prototype amplifier generated noise was measured to be 4.8 nV/Hz @ 1 kHz (0.45 μVRMS with bandwidth 10 Hz-10 kHz), which is lower than the currently used device generated noise of 7.8 nV/Hz @ 1 kHz (0.76 μVRMS). A low noise (<1 nV/Hz) radio frequency interference filter was realized to minimize noise contribution from the pre-amplifier, while maintaining the required bandwidth in high impedance measurements. Validation of the prototype device was conducted for actual ECOG recordings on humans that showed an increase (p < 0.05) of ~5 dB in Signal-to-Noise ratio (SNR), and for EVestG recordings using a synthetic ear model that showed a ~4% improvement (p < 0.01) over the currently used amplifier.

CONCLUSION

This paper presents the design and evaluation of an ultra-low noise and miniaturized bio-signal amplifier tailored for EVestG and ECOG. The increase in SNR for the implemented amplifier will reduce variability associated with bio-features extracted from such recordings; hence sensitivity and specificity measures associated with disease classification are expected to increase. Furthermore, immunity to PLI has enabled EVestG and ECOG recordings to be carried out in a non-shielded clinical environment.

Implementation of a gelatin model to simulate biological activity in the inner ear for electrovestibulography (EVestG) validation

In this work, a physical model that simulates electrical activity of the inner ear has been developed. The purpose is to evaluate extraction of vestibular field potentials (FPs) in the presence of various sources of noise by a proprietary software algorithm. The ear model is constructed of gelatin as an alternative to human tissue where independently driven electrical sources are placed in gelatin to mimic various biological signals (muscle, cerebral, and vestibular). Components of system noise (recording apparatus generated noise, electrodes, etc) will be naturally superimposed on the recording, hence enables various recording conditions to be simulated. Muscle activity present in the recordings and noise generated from the recording apparatus were found to be the most dominating sources that degrade performance of FP extraction. The model can be used to provide insights towards enhancing the FP detection algorithm under various signal-to-noise ratios.

Diagnosis of Parkinson's disease using electrovestibulography

In this paper, a new method for diagnosis of Parkinson's disease (PD) based on the analysis of electrovestibulography (EVestG) signals is introduced. EVestG signals are in fact the vestibular response modulated by more cortical brain signals; they are recorded from the ear canal. EVestG data of 20 individuals with PD and 28 healthy controls were adopted from a previous study. The field potentials and their firing pattern in response to whole body tilt stimuli from both left and right ears were extracted. We investigated several statistical and fractal features of the field potentials and also their firing interval histograms followed by one-way analysis of variance to select pairs of features showing the most significant differences between individuals with Parkinson disease and the age-matched controls. Linear discriminant analysis classification was applied to every selected feature using a leave-one-out routine. The result of each feature's classifier was used in a heuristic average voting system to diagnose PD patients. The results show more than 95% accuracy for PD diagnosis. Given that the patients were at different stage of disease, the high accuracy of the results is encouraging for continuing exploration of the EVestG application to PD diagnosis as it may provide a quick and non-invasive screening tool.