A new diagnostic vestibular evoked response

Dastgheib et al. Journal of Otolaryngology - Head and Neck Surgery (2015) 44:14 DOI 10.1186/s40463-015-0065-7 ORIGINAL RESEARCH ARTICLE Open Access A new diagnostic vestibular evoked response Zeinab A Dastgheib1*, Brian Lithgow2, Brian Blakley3 and Zahra Moussavi4 Abstract Objective: To describe the development of a new clinically applicable method for assessing vestibular function in humans with particular application in Meniere’s disease. Study design: Sophisticated signal-processing techniques were applied to data from human subject undergoing tilts stimulating the otolith organs and semicircular canals. The most sensitive representatives of vestibular function were extracted as “features”. Methods: After careful consideration of expected response features, Electrovestibulography, a modified electrocochleography, recordings were performed on fourteen Meniere’s patients and sixteen healthy controls undergoing controlled tilts. The data were subjected to multiple signal processing techniques to determine which “features” were most predictive of vestibular responses. Results: Linear discriminant analysis and fractal dimension may allow data from a single tilt to be used to adequately characterize the vestibular system. Conclusion: Objective, physiologic assessment of vestibular function may become realistic with application of modern signal processing techniques. Keywords: Meniere’s disease, EVestG, Vestibular response, Classification, Fractal dimension Introduction Vestibular disorders are among the most common reasons that patients seek the advice of a physician, yet the diagnosis of dizziness largely relies on the patient history. The patient history is subjective and its reproducibility has not been validated. Significant physiologic disruptions of neurological function should cause repeatable, measureable changes in neural activity. We believe that sophisticated and objective measurement of these changes should be diagnostic and should reveal underlying pathologic mechanisms. This paper outlines the application of advanced statistical signal processing techniques from the fields of engineering and statistics to understand normal and pathologic vestibular function using Meniere’s disease as a prototype. Evoked potentials have been successfully applied to diagnose auditory disorders but may be difficult for vestibular diagnosis. Auditory evoked potentials typically involve temporal averaging of several hundred auditory * Correspondence: 1 Department of Electrical & Computer Engineering, University of Manitoba, Room E3-512 Eng. Bldg., 75A Chancellor’s Circle, Winnipeg, MB R3T 5V6, Canada Full list of author information is available at the end of the article stimuli which may be problematic in vestibular stimuli. On the other hand, when observing the averaging process of auditory evoked potentials in real time, the first response or two are often adequate to see the general nature of the response. It would seem then that the first response or two should contain diagnostic information if it could be extracted. With this observation in mind is seems plausible that sophisticated signal processing techniques might be able tease out enough information from a few tilts to permit recognition of repeatable patterns of waveforms that could be diagnostically useful. Electrocochleography (ECoG) is a diagnostic evokedpotential method that records an excitatory ‘gross’ evoked response by averaging responses to a series of auditory clicks [1-3]. A useful, analagous vestibular test would directly measure the dynamic response of the vestibular system to both excitatory and inhibitory inputs, and derive a measure of its dynamic range. Electrovestibulography (EVestG) [4,5] is similar to ECoG but the multiple acoustic stimuli are replaced by one or two passive whole body tilts in a hydraulically controlled chair located in an electrically and acoustically shielded chamber. The EVestG signal is recorded during dynamic and static phases via © 2015 Dastgheib et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Dastgheib et al. Journal of Otolaryngology - Head and Neck Surgery (2015) 44:14 ECoG electrodes resting near the tympanic membrane of both ears [6]. Figure 1, shows the recording system with the hydraulic chair. A proprietary software algorithm called the “Neural Event Extraction Routine (NEER)” [5] has been developed to extract the field potential (FP) signals from the EVestG recordings. NEER algorithm derives two signals from the recording raw signals: the averaged response of FPs and the time intervals between the FPs. Pattern recognition techniques applied to EVestG signals have shown very encouraging results in other neurological diagnostic applications such as Parkinson’s disease, depression, and schizophrenia disorder by other studies [7-9]. In this paper will apply EvestG techniques to Meniere’s disease patients with a view to developing an objective test for the disorder. Usually several features as biomarkers are extracted from the output of the NEER algorithm on the EVestG signals. Most diagnostic tests measure the signals’ most important parameters to classify a system as normal or abnormal. The “feature” extraction technique utilizes many quantitative criteria from the signal to categorize the response. Extracted criteria may be statistical parameters, calculations of some characteristic of the waveform or derivations from multiple other sources. The technique of “feature extraction” is similar to that used in cochlear implants. Herein, we apply it to vestibular function. A major difficutly in measuring biological electrical potentials is Page 2 of 7 the signal-to-noise ratio. We are trying to detect a small signal in the midst of tremendous electrical noise from nerve, muscle and other cells. In this paper we discuss the clinical utility of NEER algorithm and EVestG extracted signals. First we briefly describe of the key concepts. Further details can be found in the Additional file 1. Features In signal processing, features are quantities that are associated with a signal or a process. Features may be statistical measures such as the mean, standard deviation, skewness, kurtosis, etc. of a statistical process, or they may be other quantitative measures representing fractal nature, power distribution, etc. of a signal or process. In addition to statistical features, this report includes features representing fractal dimension (FD) as assessed by the Higuchi fractal dimension (HFD), and entropy-based dimensions such as the Infor (...truncated)