Simulation of the Physiological Characteristics of Pillar and Modiolar Fibers of the Auditory Nerve.

The study of the physiological characteristics of the auditory nerve fibers is fundamental to understand their capability to encode sounds. These characteristics include their spontaneous firing rate, their threshold, and their dynamic range. Although it is possible to perform in vitro recordings of these characteristics in different cell models, it is complicated to obtain in vivo measurements of them directly from the cochlea. For example, the apex of the cochlea since it is an unreachable region which is vulnerable to surgical trauma that could result in altered recordings. In this paper, the behavior of Pillar and Modiolar fibers of the auditory nerve were simulated in response to tone bursts of different frequencies and intensities. The proposed model allowed us to associate the basal firing rates with the physiological characteristics of the different auditory nerve fibers. This is especially important since some noise-associated hearing losses, such as acoustic trauma, have been explained as selective fiber damages.Clinical Relevance- Models that describe the properties of auditory nerve fibers are important to study specific aspects of maturation as well as the causes of sensorineural hearing loss in humans.

Electrical Cochlear Response Consistency from different Cochlear Implant Users.

The Electrical Cochlear Response (ECR) is a scalp potential recently described in the literature which offers an alternative approach for objective adaptation of Cochlear Implant (CI) to individual patient requirements. Thus it is necessary to know about the consistency of this response across implanted patients using devices with different design criteria. This work shows that the ECR wave shape morphology is not affected by CI manufacture design differences. For this purpose and to contend with the sensibility to electric stimulation change along the cochlea, six contiguous intracochlear electrodes located at the apical end of the cochlea were studied. According to the CI manufacturer, the population of twelve implanted pediatric patients was divided into three groups. Artifacts due to the CI stimulation pip tone and operation during ECR acquisition were canceled using the Empirical Mode Decomposition method. For wave shape morphology comparison among electrodes, ECR amplitude was normalized, and the average intra- and inter-user group ECR Correlations were calculated. Intra and inter-group Correlation coefficient goes from 0.58 to 0.9 and from 0.63 to 0.85, respectively. For the same patient and group Correlation coefficient between ECR of the electrode located at the apical end of the cochlea and adjacent electrodes decreases from apex to base. These results support the consistency of the ECR waveshape morphology across users of different CI types.Clinical Relevance- ECR offers a new objective methodology for the initial programming and later readjustment of electrical stimulation provided by the cochlear implant. The patient uses the device in daily operation mode; the scenery is quite impossible with the current resources for evaluating CI performance. This methodology is compatible with all current CIs without special hardware or software requirements due to different devices type. It can be applied any time after initial device programming, regardless of patient age or previous training. Therefore, it is important to know that ECR wave shape morphology is not affected by the differences in design and operation of current cochlear stimulation systems.

Simulation of the efficiency of inner hair cell secretion in the auditory pathway.

Sound coding involves several stages of processing along the auditory path. Specifically, the Inner Hair Cells (IHC) act as sensory receptors and transduce acoustic information -frequency, intensity and duration of the stimulus- into neuronal signals. In this work, a stochastic model was implemented to achieve a better understanding of the IHC-auditory nerve synapse, specifically, the process of Ready Releasable Pool (RRP) vesicle exocytosis, a complicated process to study experimentally because current protocols do not provide adequate temporal resolution, in the order of milliseconds. The presented model allows predicting the efficiency of glutamate release towards explaining maturation changes or disease impacts in the auditory pathway.

Cochlear implant artifact reduction on one channel Mismatch Negativity recordings based on Ensemble Empirical Mode Decomposition and Independent Component Analysis.

Artifact generated by cochlear implants has been a problem for being able to register Mismatch Negativity (MMN) response. There are methods for reducing the artifact using multiple channels from the EEG but in this paper are presented the first results of a method using only the channel with the artifact using Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA). The first results showed that it was possible to get the MMN registers from the group of normal recordings and partially with the group of recordings from patients with cochlear implant. It is possible to suggest that EEMD in conjunction with ICA can be used for studies searching MMN.

Uso del Análisis por Componentes Independientes en la extracción de artefactos de la respuesta Mismatch Negativity / Use of Independent Component Analysis in the extraction of Artifacts of the Mismatch Negativity Response

Resumen: El Potencial de disparidad es una respuesta cortical elicitada por la detección automática de estímulos con distintas características, permitiendo la exploración de procesos neuropsicológico. Sin embargo el análisis de esta señal se puede dificultar por una baja relación señal a ruido debida a los artefactos presentes en la adquisición de la misma. Diversas publicaciones proponen el uso de implementaciones de la técnica de Separación Ciega de fuentes, como el Análisis por Componentes Independientes (ACI), para preprocesar las señales y eliminar estos artefactos. Sin em bargo, no se ha estudiado cuál de los algoritmos ACI que se encuentran en la literatura será el óptimo para mejorar la calidad del MMN, por lo que en este estudio se propuso determinar si existen diferencias significativas en las respuestas obtenidas al utilizar los algoritmos de FastICA, Infomax y SOBI para eliminar los artefactos típicamente presentes en este tipo de señales. Adicionalmente se dan algunas características de estos artefactos a manera de sistematizar la identificación y eliminaciones de los mismos, además de comparar las respuestas obtenidas con y sin preprocesamiento, así como la distribución topográfica de este potencial antes y después de la eliminación de artefactos. Mediante el algoritmo Infomax se identifican mejor los Componentes Independientes asociados con artefactos, resultando en un MMN de mayor amplitud y distribución topográfica fronto-central con predominancia izquierda.

Abstract: Mismatch Negativity is a cortical response elicited by the automatic detection of stimuli which have different characteristics, allowing exploration of neuropsychological processes. However, the analysis of this signal can be di fficult by a low SNR due to artifacts present when the signal is recorded. Different publications propose to use the approach given by the Blind Source Separation Technique by means of the Independent Component Analysis (ICA) to preprocess and eliminate these artifacts. Nevertheless, it has not been studied which of the ICA algorithms found in the literature will be optimal for improving the quality of MMN. Therefore the aim of this study is to determine whether there are significant differences in the responses obtained by using FastICA, Infomax and SOBI to remove artifacts typically present in such signals. In addition, some features of the Independent Components related to artifacts are given in order to systematize the identification and elimination of those. In addition, MMN responses obtained with and without data preprocessing, as well as topographic maps before and after the elimination of artifacts were compared. Thus, Infomax is the best ICA algorithm to calculate Independent Components associated with artifacts, resulting in high amplitude MMN and a topographic map with a clear fronto-central distribution with left-hemisphere predominance.

Efecto del pre-procesamiento del EEG en el Análisis por Componentes Independientes: reducción del artefacto del implante coclear en los Potenciales Evocados Auditivos / Effect of EEG pre-processing on Independent Component Analysis: reduction of cochlear implant artifact in Auditory Evoked Potentials

Resumen: El Análisis por Componentes Independientes (ICA, Independent Component Analysis) es una herramienta muy utilizada para eliminar los artefactos comunes del EEG, sin embargo existe poca bibliografía sobre el impacto que tienen etapas de pre-procesamiento de esta señal sobre el desempeño del ICA. En este trabajo se comparó el efecto de aplicar dos filtros digitales diferentes, pasabajas y pasabanda, en una etapa de procesamiento previa a ICA, para remover específicamente el artefacto de un implante coclear en registros de Potenciales Evocados Auditivos. Se analizaron señales de 10 sujetos usuarios de implante coclear y en 5 de estos registros con el pre-filtrado pasabajas se obtuvieron los mayores valores del índice de la Relación Señal Interferencia, utilizado para evaluar la calidad de la separación. El mayor efecto al remover el artefacto del implante coclear se nota en los electrodos T4 y T6, que corresponde a la zona donde los sujetos tienen colocado su implante (área temporal derecha).

Abstract: Independent Component Analysis (ICA) is an algorithm used to remove artifacts from the EEG. However, there is little current literature about the impact of preprocessing stages of this signal on the performance of ICA. In this paper the effect of applying two different digital filters - lowpass and bandpass -, in a pre-processing step to ICA, was compared. This to remove the cochlear implant artifact from the Auditory Evoked Potentials. Recordings from 10 cochlear implant users were analyzed. In 5 of these records using the pre-lowpass filtering, the highest Signal Interference Ratio (SIR) was obtained; this index was used to assess the quality of ICA separation. The greatest effect of removing the cochlear implant artifact is noted in both T4 and T6 electrodes, which correspond to the area where the subjects have placed their implants (right temporal area).

On the Identification of an ICA Algorithm for Auditory Evoked Potentials Extraction: A Study on Synthetic Data / Identificación de un algoritmo ICA para la extracción de potenciales evocados auditivos: un estudio en señales sintéticas

Extracting characteristics and information from Auditory Evoked Potentials recordings (AEPs) involves difficulties due to their very low amplitude, which makes the AEPs easily hidden by artifacts from physiological or external sources like the EEG/EMG, blinking, and line-noise. To tackle this problem, some authors have used Independent Component Analysis (ICA) to successfully de-noise brain signals. However, since interest has been mainly focused on removing artifacts like blinking, not much attention has been paid to the quality of the recovered evoked potential. This is the AEP case, where literature reports interesting results on the de-noising matter, but without an objective evaluation of the AEP finally extracted (and the influence of different implementations or configurations of ICA). Here, to study the performance of three popular ICA algorithms (FastICA, Ext-Infomax, and SOBI) at separating AEPs from a mixture, a synthetic dataset composed of one Long Latency Auditory Evoked Potential (LLAEP) signal and the most frequent artifacts was generated. Next, the quality of the independent components (ICs) estimated by such algorithms was measured by using the AMARI performance index (Am), the signal interference ratio index (SIR), and the time required to achieve separation. Results indicated that the FastICA implementation, with the symmetric approach and the power cubic contrast function, is more likely to provide the best and faster separation of the LLAEP, which makes it suitable for this purpose.

La extracción de características e información de los registros de Potenciales Evocados Auditivos (AEPs) es complicada debido a su baja energía, la que lo hace fácilmente enmascarable por artefactos de origen fisiológico o externo, como el EEG/EMG, el parpadeo y el ruido de línea. Este problema ha sido abordado por algunos autores mediante el uso del Análisis por Componentes Independientes (ICA, por sus siglas en inglés), que se ha utilizado principalmente para reducir artefactos. Estos trabajos han enfocado su interés en la tarea de remover artefactos como el parpadeo, por lo que han descuidado el estudio de la calidad del potencial evocado recuperado. Este es el caso del AEP, donde aun cuando la literatura reporta resultados interesantes en la reducción de artefactos, no existe una evaluación objetiva del AEP finalmente extraído (y el efecto de usar diferentes implementaciones/configuraciones de ICA). En este trabajo, con el objetivo de cuantificar el desempeño de tres algoritmos de ICA (FastICA, Ext-Infomax, y SOBI) en la calidad de la separación de los AEPs, se generó una mezcla sintética de señales compuesta por un Potencial Evocado Auditivos de Latencia Larga (LLAEP) y artefactos frecuentemente presentes en estos registros. Después, se cuantificó la calidad de los componentes independientes (ICs, por sus siglas en inglés) estimados por estos algoritmos utilizando el índice de desempeño (AMARI, por sus siglas en inglés) el índice de la relación de interferencia entre señales (SIR, por sus siglas en inglés) y el tiempo requerido para realizar la separación. Los resultados indican que FastICA, con el enfoque simétrico y la función de contraste potencia cúbica, proporciona la mejor y más rápida separación del LLAEP, lo que lo vuelve idóneo para esta tarea.

An assessment strategy for proposals of engineering projects in the Bachelor of Biomedical Engineering Curriculum at Universidad Autónoma Metropolitana-Iztapalapa.

Since 1974, the Bachelor of Biomedical Engineering Program (BBME) is offered at Universidad Autónoma Metropolitana-Iztapalapa, in Mexico City. By design, it must be completed in four years (12 trimesters) and, in the latter three, the senior students work on a BME project, which is done by completing three modules: Project Seminar (PS), Project on BME I and Project on BME II. In the PS module, the student must find a problem of interest in the BME field and suggest a solution through the development of an Engineering Project Proposal (EPP). Currently, the module is being taught by two faculty members of the BBME, who instruct students on how to develop their EPPs and evaluate their progress by reviewing a number of EPPs during the trimester. This generates a huge workload for the module instructors, which makes it necessary to involve more faculty members trimester-to-trimester (i.e. every 12 weeks) and, therefore, to create a set of systematic guidelines that ease the evaluation process for new instructors. Hence, the purpose of this paper is to present an assessment strategy (in the form of an assessment matrix) for the PS module as well as some preliminary results after two trimesters of its implementation.

Comparison between different similarity measure functions for optimal clustering AEPs Independent Components.

A critical part of applying Independent Component Analysis (ICA) to any neurophysiological data is the selection of relevant independent Components (ICs); i. e., to decide which ICs have neurological meaning. Standard ICA implementation supposes a square mixing matrix; this results in as many ICs as EEG channels. In this work, responses to repetitive auditory stimuli are the most important signals (Auditory Evoked Potentials, AEPs); so the ICs of interest should be repetitive and time-locked with the stimuli. In this paper an update of a previously proposed procedure for the objective selection of ICs using Mutual Information (MI) and cluster analysis is presented. This time, four different similarity functions are evaluated and three inter/intra-cluster quality criteria are explored to determine optimal cluster numbers to both synthetic AEPs and data from normal hearing children, so that to identify ICs related with the auditory response. The numbers of clusters and the similarity function that yield best results in both datasets, in other words optimal clustering AEPs ICs, were 8 and Euclidean link-clustering average respectively.

Adventitious lung sounds imaging by ICA-TVAR scheme.

Adventitious lung sounds (ALS) as crackles and wheezes are present in different lung alterations and their automated characterization and recognition have become relevant. In fact, recently their 2D spatial distribution (SD) imaging has been proposed to help diagnose of pulmonary diseases. In this work, independent component analysis (ICA) by infomax was used to find crackles sources and from them to apply a time variant autoregressive model (TVAR) to count and imaging the ALS. The proposed methodology was assessed on multichannel LS recordings by embedding simulated fine crackles with known SD in recorded normal breathing sounds. Afterwards, the adventitious image of two patients with fibrosis and emphysema were obtained and contrasted with the classical pulmonary auscultation provided by a pneumologist. The results showed that combining ICA and TVAR leads to a robust methodology to imaging ALS.

Assessment of ICA algorithms for the analysis of crackles sounds.

Blind source separation by independent component analysis has been applied extensively in the biomedical field for extracting different contributing sources in a signal. Regarding lung sounds analysis to isolate the adventitious sounds from normal breathing sound is relevant. In this work the performance of FastICA, Infomax, JADE and TDSEP algorithms was assessed using different scenarios including simulated fine and coarse crackles embedded in recorded normal breathing sounds. Our results pointed out that Infomax obtained the minimum Amari index (0.10037) and the maximum signal to interference ratio (1.4578e+009). Afterwards, Infomax was applied to 25 channels of recorded normal breathing sound where simulated fine and coarse crackles were added including acoustic propagation effects. A robust blind crackle separation could improve previous results in generating an adventitious acoustic thoracic imaging.

The selection of optimal ICA algorithm parameters for robust AEP component estimates using 3 popular ICA algorithms.

Many authors have used the Auditory Evoked Potential (AEP) recordings to evaluate the performance of their ICA algorithms and have demonstrated that this procedure can remove the typical EEG artifact in these recordings (i.e. blinking, muscle noise, line noise, etc.). However, there is little work in the literature about the optimal parameters, for each of those algorithms, for the estimation of the AEP components to reliably recover both the auditory response and the specific artifacts generated for the normal function of a Cochlear Implant (CI), used for the rehabilitation of deaf people. In this work we determine the optimal parameters of three ICA algorithms, each based on different independence criteria, and assess the resulting estimations of both the auditory response and CI artifact. We show that the algorithm utilizing temporal structure, such as TDSEP-ICA, is better in estimating the components of the auditory response, in recordings contaminated by CI artifacts, than higher order statistics based algorithms.

Objective source selection in Blind Source Separation of AEPs in children with Cochlear Implants.

Multi-channel Auditory Evoked Potentials (AEPs) are a useful methodology for evaluating the auditory performance of children with Cochlear Implants (CIs). These recordings are generally contaminated, not only with well known physiological artifacts (blinking, muscle) and line noise etc., but also by CI artifact. The CI induces an artifact in the recording at the electrodes in the temporal lobe area (where it is implanted) when specific tones are presented, this artifact in particular makes the detection and analysis of AEPs much more challenging. This paper evaluates the convenience of using Blind Source Separation (BSS) and Independent Component Analysis (ICA) in order to identify the AEPs from ongoing recordings and to isolate the artifact when testing a child with a CI. We propose a new procedure to elicit an objective differentiation between the independent components (ICs) related to the AEPs and CI artifact; two concepts are fundamental in this procedure Mutual Information (MI) and Clustering. Finally, the variability of three BSS/ICA algorithms is assessed; in order to determine which one is more convenient to isolate the respective ICs of interest. Temporal decorrelation based ICA showed the least change in the estimation of both the AEPs and the CI artifact; this has allowed for considerable autonomy in the construction of relevant, consistent clusters.