David E. Lerner

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We describe a procedure (and the motivation behind it) which rapidly and accurately tracks the onset and progress of an epileptic seizure. Roughly speaking, one monitors changes in the relative dispersion of a re-embedded time series. The results are robust with respect to variation of adjustable parameters such as embedding dimension , lag time, and(More)
In this paper, we examine the non-linearity of mechano-electric transduction in the cochlea by computing the instantaneous frequency (IF) of the cochlear microphonic (CM) in response to sinusoidal stimuli. In contrast to a linear system which yields a constant IF when driven with a sinusoid, the IF of the CM varied during one period of oscillation. This(More)
Innovative applications of non-linear time series analysis have recently been used to investigate physiological phenomena. In this study, we investigated the feasibility of using the correlation integral to monitor the localized muscle fatigue process in the biceps brachii during sustained maximal efforts. The subjects performed isometric maximum(More)
As regularly spaced time series imagery becomes more prevalent in the remote sensing community, monitoring these data for temporal consistency will become an increasingly important problem. Long-term trends must be identified, and it must be determined if such trends correspond to true changes in reflectance characteristics of the study area (natural), or(More)
We present an algorithm called the median transform which can be used to decompose the round window auditory potential into AC and DC components. The first of these is identified with the cochlear microphonic, and the second with the combined summating and compound action potentials. Elsewhere in this volume, the algorithm is employed as an intermediate(More)
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