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  • Title: A Time-Saving Alternative to "Peak-Picking" Algorithms: A Gaussian Mixture Model Feature Extraction Technique for the Neurodiagnostic Auditory Brainstem Response.
    Author: Kamerer AM.
    Journal: Ear Hear; ; 45(5):1115-1124. PubMed ID: 38419164.
    Abstract:
    OBJECTIVES: The accurate and efficient analysis of neurodiagnostic auditory brainstem responses (ABR) plays a critical role in assessing auditory pathway function in human and animal research and in clinical diagnosis. Traditional analysis of the neurodiagnostic ABR analysis involves visual inspection of the waveform and manually marking peaks and troughs. Visual inspection is a tedious and time-consuming task, especially in research where there may be hundreds or thousands of waveforms to analyze. "Peak-picking" algorithms have made this task faster; however, they are prone to the same errors as visual inspection. A Gaussian mixture model-based feature extraction technique (GMM-FET) is a descriptive model of ABR morphology and an alternative to peak-picking algorithms. The GMM-FET is capable of modeling multiple waves and accounting for wave interactions, compared with other template-matching approaches that fit single waves. DESIGN: The present study is a secondary analysis applying the GMM-FET to 321 ABRs from adult humans from 2 datasets using different stimuli and recording parameters. Goodness-of-fit of the GMM-FET to waves I and V and surrounding waves, that is, the summating potential and waves IV and VI, was assessed, and latency and amplitude estimations by the GMM-FET were compared with estimations from visual inspection. RESULTS: The GMM-FET had a similar success rate to visual inspection in extracting peak latency and amplitude, and there was low RMS error and high intraclass correlation between the model and response waveform. Mean peak latency differences between the GMM-FET and visual inspection were small, suggesting the two methods chose the same peak in the majority of waveforms. The GMM-FET estimated wave I amplitudes within 0.12 µV of visual inspection, but estimated larger wave V amplitudes than visual inspection. CONCLUSIONS: The results suggest the GMM-FET is an appropriate method for extracting peak latencies and amplitudes for neurodiagnostic analysis of ABR waves I and V.
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