Isaac Alvarez

Using evoked compound action potentials to assess activation of electrodes and predict C-levels in the Tempo+ cochlear implant speech processor.

Objective: In this study, we analyze how electrically evoked compound action potential (ECAP) responses can be used to assess whether electrodes should be activated in the map and to estimate C levels in the Med-El Tempo+ Cochlear Implant Speech Processor. Design: ECAP thresholds were measured using the ECAP Recording System of the Pulsar CI100 implant. Twenty-one postlingually and 28 prelingually deafened patients participated in this study. The relationship between ECAP responses and the activation of electrodes was analyzed. Because an error in the estimation of T levels (behavioral thresholds) has less effect on hearing quality than an error in the estimation of C levels in the Tempo+ cochlear implant speech processor (maximum comfort levels), correlation and regression analyses were performed between ECAP thresholds and C levels. Results: The observation of an evoked potential generally implied that the electrode was activated because only 3.5% of electrodes that yielded measurable evoked responses were deactivated, because of collateral stimulations or an unpleasant hearing sensation. In contrast, the absence of an evoked potential did not imply that an electrode should be deactivated, because 20% of these electrodes provided a useful auditory sensation. ECAP responses did not predict the absolute behavioral comfort levels because of the excessive error between behavioral C levels and those derived from ECAP thresholds (the mean relative error is 43.78%). However, by applying a normalization procedure, ECAP measurements allowed the C-level profile to be predicted with a mean relative error of 6%; that is, they provided useful data to determine the C level of each electrode relative to the average C level of the patient. Conclusions: ECAP is a reliable and an useful objective measurement that can assist in the fitting of the Tempo+ cochlear implant speech processor. From results presented in this work, a protocol is proposed for fitting this cochlear implant system. This protocol facilitates appropriate cochlear implant fitting, particularly for children or uncooperative patients.

Long-Term Evolution of the Electrical Stimulation Levels for Cochlear Implant Patients

Objectives The stimulation levels programmed in cochlear implant systems are affected by an evolution since the first switch-on of the processor. This study was designed to evaluate the changes in stimulation levels over time and the relationship between post-implantation physiological changes and with the hearing experience provided by the continuous use of the cochlear implant. Methods Sixty-two patients, ranging in age from 4 to 68 years at the moment of implantation participated in this study. All subjects were implanted with the 12 channels COMBI 40+ cochlear implant at San Cecilio University Hospital, Granada, Spain. Hearing loss etiology and progression characteristics varied across subjects. Results The analyzed programming maps show that the stimulation levels suffer a fast evolution during the first weeks after the first switch-on of the processor. Then, the evolution becomes slower and the programming parameters tend to be stable at about 6 months after the first switch-on. The evolution of the stimulation levels implies an increment of the electrical dynamic range, which is increased from 15.4 to 20.7 dB and improves the intensity resolution. A significant increment of the sensitivity to acoustic stimuli is also observed. For some patients, we have also observed transitory changes in the electrode impedances associated to secretory otitis media, which cause important changes in the programming maps. Conclusion We have studied the long-term evolution of the stimulation levels in cochlear implant patients. Our results show the importance of systematic measurements of the electrode impedances before the revision of the programming map. This report also highlights that the evolution of the programming maps is an important factor to be considered in order to determine an adequate calendar fitting of the cochlear implant processor.

Generalized alternating stimulation: a novel method to reduce stimulus artifact in electrically evoked compound action potentials.

Stimulus artifact is one of the main limitations when considering electrically evoked compound action potential for clinical applications. Alternating stimulation (average of recordings obtained with anodic-cathodic and cathodic-anodic bipolar stimulation pulses) is an effective method to reduce stimulus artifact when evoked potentials are recorded. In this paper we extend the concept of alternating stimulation by combining anodic-cathodic and cathodic-anodic recordings with a weight in general different to 0.5. We also provide an automatic method to obtain an estimation of the optimal weights. Comparison with conventional alternating, triphasic stimulation and masker-probe paradigm shows that the generalized alternating method improves the quality of electrically evoked compound action potential responses.

Discriminative Feature Selection for Automatic Classification of Volcano-Seismic Signals

Feature extraction is a critical element in automatic pattern classification. In this letter, we propose different sets of parameters for classification of volcano-seismic signals, and the discriminative feature selection (DFS) method is applied for selecting the minimum number of features containing most of the discriminative information. We have applied DFS to a conventional cepstral-based parameterization (with 39 features) and to an extended set of parameters (including 84 features). Classification experiments using seismograms recorded at Colima Volcano (Mexico) show that, for the most complex classifier and using the cepstral-based parameterization, DFS provided a reduction of the error rate from 24.3% (using 39 features) to 15.5% (ten components). When DFS is applied to the extended parameterization, the error rate decreased from 27.9% (84 features) to 13.8% (14 features). These results show the utility of DFS for identifying the best components from the original feature vector and for exploring new parameterizations for the classification of volcano-seismic signals.

An Automatic P-Phase Picking Algorithm Based on Adaptive Multiband Processing

This letter presents a novel picking algorithm which allows an automated determination of the P-phase onset time. The algorithm includes an adaptive multiband processing and noise-reduction techniques to allow a confident onset time estimation in signals strongly affected by background and/or nonstationary noise processes. Results using a set of 3780 computer-generated earthquake-like signals show that the accuracy is much better than that achieved by conventional STA/LTA algorithm. In addition, the accuracy of the proposed method is improved when it is combined with an autoregressive method. An application of the algorithm to a set of 400 natural earthquakes confirms that the combination of both algorithms provides a precise P-phase onset time estimation in real environments, overcoming the limitations associated with the autoregressive method.

A study of adaptation mechanisms based on ABR recorded at high stimulation rate.

OBJECTIVE This paper analyzes the fast and slow mechanisms of adaptation through a study of latencies and amplitudes on ABR recorded at high stimulation rates using the randomized stimulation and averaging (RSA) technique. METHODS The RSA technique allows a separate processing of auditory responses, and is used, in this study, to categorize responses according to the interstimulus interval (ISI) of their preceding stimulus. The fast and slow mechanisms of adaptation are analyzed by the separated responses methodology, whose underlying principles and mathematical basis are described in detail. RESULTS The morphology of the ABR is influenced by both fast and slow mechanisms of adaptation. These results are consistent with previous animal studies based on spike rate. CONCLUSIONS Both fast and slow mechanisms of adaptation are present in all subjects. In addition, the distribution of the jitter and the sequencing of the stimuli may be critical parameters when obtaining reliable ABRs. SIGNIFICANCE The separated responses methodology enables for the first time the analysis of the fast and slow mechanisms of adaptation in ABR obtained at stimulation rates greater than 100 Hz. The non-invasive nature of this methodology is appropriate for its use in humans.

A Comparative Study of Dimensionality Reduction Algorithms Applied to Volcano-Seismic Signals

Detection and classification of the different seismic events are important tasks in volcanological observatories. Trying to make these an automatic process is fundamental for the volcanological community. It is crucial to choose how the seismic signal is represented in terms of parameters or features useful for dealing with the automatic classification problem, since the number and type of parameters could be really large leading to the curse of dimensionality issue. Machine learning theory establishes that in order to build a classifier from a labeled database, there should be a compromise between the complexity of the classifier and the size of the database. Since generating a manually labeled database is a tedious work performed by specialists in volcanology, the size of the databases limits the complexity of the classification systems built by them. On the other hand, if the databases could be represented by a reduced, but relevant, number of features, the complexity of the classifier would be simplified. In order to study the problem just described, this paper performs a comparative study of different classical techniques of dimensionality reduction (DR) of the feature set. The algorithms implemented include feature selection techniques as wrappers and filters and methods which directly transform the original feature space into another with lower dimension. All algorithms have been tested using an automatic classification system of volcano-seismic events. The best results have been obtained with the discriminative feature selection (DFS) algorithm which belongs to the set of wrapper methods.

APASVO: A free software tool for automatic P-phase picking and event detection in seismic traces

Abstract The accurate estimation of the arrival time of seismic waves or picking is a problem of major interest in seismic research given its relevance in many seismological applications, such as earthquake source location and active seismic tomography. In the last decades, several automatic picking methods have been proposed with the ultimate goal of implementing picking algorithms whose results are comparable to those obtained by manual picking. In order to facilitate the use of these automated methods in the analysis of seismic traces, this paper presents a new free, open source, software graphical tool, named APASVO, which allows picking tasks in an easy and user-friendly way. The tool also provides event detection functionality, where a relatively imprecise estimation of the onset time is sufficient. The application implements the STA-LTA detection algorithm and the AMPA picking algorithm. An autoregressive AIC-based picking method can also be applied. Besides, this graphical tool is complemented with two additional command line tools, an event picking tool and a synthetic earthquake generator. APASVO is a multiplatform tool that works on Windows, Linux and OS X. The application can process data in a large variety of file formats. It is implemented in Python and relies on well-known scientific computing packages such as ObsPy, NumPy, SciPy and Matplotlib.

Evaluating robustness of a HMM-based classification system of volcano-seismic events at colima and popocatepetl volcanoes

This work presents a continuous volcano-seismic classification system based in the Hidden Markov Models as solution to recently strong needs for automatic event detection and recognition methods in early warning and monitoring scenarios. Furthermore, our system includes a reliable method to assign confidence measures to the recognized signals in order to evaluate the robustness of the results. Data from the two most active volcanoes have been used to probe the system reliability on a complex joint corpus achieving a recognition accuracy higher than 78% in blind recognition tests.

Improving feature extraction in the automatic classification of seismic events. Application to Colima and Arenal volcanoes

Monitoring of precursory seismicity in volcanoes is the most reliable and widely used technique in volcano monitoring. Since a visual inspection by human operators is a tedious task in a non-stop monitoring process, Hidden Markov Models have been previously proposed to automatically classify the different types of volcano-seismic events. Mel Frequency Cepstral Coefficients were successfully used as feature vector in this continuous classification system. In this paper seven novel features to be included in the MFCC feature vector are proposed. A very elementary GMM-based classifier has been implemented in order to assess the efficiency of the proposed parameters. Results using hundreds of events recorded from stations situated at Colima (Mexico) and Arenal (Costa Rica) volcanoes show that the proposed features improve the recognition accuracy and therefore they may be relevant in continuous volcano-seismic event automatic classification.