Improving quality of life in the elderly: hearing loss treatment with cochlear implants.

BACKGROUND: Hearing loss impacts health-related quality of life and general well-being and was identified in a Lancet report as one of the largest potentially modifiable factors for the prevention of age-related dementia. There is a lack of robust data on how cochlear implant treatment in the elderly impacts quality of life. The primary objective was to measure the change in health utility following cochlear implantation in individuals aged ≥ 60 years. METHODS: This study uniquely prospectively recruited a large multinational sample of 100 older adults (mean age 71.7 (SD7.6) range 60-91 years) with severe to profound hearing loss. In a repeated-measures design, pre and post implant outcome measures were analysed using mixed-effect models. Health utility was assessed with the Health Utilities Index Mark III (HUI3). Subjects were divided into groups of 60-64, 65-74 and 75 + years. RESULTS: At 18 months post implant, the mean HUI3 score improved by 0.13 (95%CI: 0.07-0.18 p < 0.001). There was no statistically significant difference in the HUI3 between age groups (F[2,9228] = 0.53, p = 0.59). The De Jong Loneliness scale reduced by an average of 0.61 (95%CI: 0.25-0.97 p < 0.014) and the Lawton Instrumental Activities of Daily Living Scale improved on average (1.25, 95%CI: 0.85-1.65 p < 0.001). Hearing Handicap Inventory for the Elderly Screening reduced by an average of 8.7 (95%CI: 6.7-10.8, p < 0.001) from a significant to mild-moderate hearing handicap. Age was not a statistically significant factor for any of the other measures (p > 0.20). At baseline 90% of participants had no or mild depression and there was no change in mean depression scores after implant. Categories of Auditory perception scale showed that all subjects achieved a level of speech sound discrimination without lip reading post implantation (level 4) and at least 50% could use the telephone with a known speaker. CONCLUSIONS: Better hearing improved individuals' quality of life, ability to communicate verbally and their ability to function independently. They felt less lonely and less handicapped by their hearing loss. Benefits were independent of age group. Cochlear implants should be considered as a routine treatment option for those over 60 years with bilateral severe to profound hearing loss. TRIAL REGISTRATION: ClinicalTrials.gov ( http://www. CLINICALTRIALS: gov/ ), 7 March 2017, NCT03072862.

The French MBAA2 sentence recognition in noise test for cochlear implant users.

OBJECTIVE: The "Marginal benefit from acoustic amplification" version 2 (MBAA2) sentence test has been used in France in the routine evaluation of cochlear implant (CI) users for 20 years. Here we present four studies that characterise and validate the test, and compare it with the French matrix sentence test. DESIGN AND SAMPLE: An analytic method was developed to obtain speech recognition threshold in noise (SNR50) from testing at a fixed signal to noise ratios (SNRs). Speech recognition was measured at several fixed SNRs in 18 normal-hearing listeners and 15 CI listeners. Then, the test-retest reliability of the MBAA2 was measured in an additional 15 CI listeners. Finally, list equivalence was evaluated in eight CI listeners. RESULTS: The MBAA2 test produced lower SNR50s and SNR50s were obtained in more CI listeners than with the French matrix test. For the MBAA2, the standard deviation of test-retest differences in CI listeners was around 1 dB SNR. Three lists had deviant difficulty and nine low item-to-total correlations. CONCLUSIONS: We propose to reduce the number of MBAA2 test lists to reduce variability. The MBAA2 test has high test-retest reliability for percent correct and SNR50, and is suitable for the assessment of cochlear implant patients.

Objective selection of EEG late potentials through residual dependence estimation of independent components.

This paper presents a novel method to objectively select electroencephalographic (EEG) cortical sources estimated by independent component analysis (ICA) in event-related potential (ERP) studies. A proximity measure based on mutual information is employed to estimate residual dependences of the components that are then hierarchically clustered based on these residual dependences. Next, the properties of each group of components are evaluated at each level of the hierarchical tree by two indices that aim to assess both cluster tightness and physiological reliability through a template matching process. These two indices are combined in three different approaches to bring to light the hierarchical structure of the cluster organizations. Our method is tested on a set of experiments with the purpose of enhancing late positive ERPs elicited by emotional picture stimuli. Results suggest that the best way to look for physiologically plausible late positive potential (LPP) sources is to explore in depth the tightness of those clusters that, taken together, best resemble the template. According to our results, after brain sources clustering, LPPs are always identified more accurately than from ensemble-averaged raw data. Since the late components of an ERP involve the same associative areas, regardless of the modality of stimulation or specific tasks administered, the proposed method can be simply adapted to other ERP studies, and extended from psychophysiological studies to pathological or sport training evaluation support.

Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies.

Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time-frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.

Facilitated light microscopic cytochemical diagnosis of acute myelogenous leukemia.

Hydroperoxidase-positive Phi bodies and rods are much more prominent and prevalent than rods visualized with a Romanovsky-type stain (Auer rods) in immature leukocytes of patients with active acute myelogenous leukemia (AML). They are readily observed with the light microscope in peripheral blood or marrow films of AML patients stained to show their peroxidatic activity. In many of these patients, Auer rods, which apparently constitute only a small subpopulation of the hydroperoxidase-positive Phi bodies and rods, were detected with difficulty, if at all. The hydroperoxidase-positive Phi bodies and rods were observed in 92% of 36 patients with active disease. They were never observed in leukocytes of patients with other hematopoietic disorders or of normal individuals. Thus, they facilitated the distinction of AML from acute lymphocytic leukemia and chronic granulocytic leukemia in blast crisis. They were absent in full clinical remission after chemotherapy and were greatly diminished in partial remission. They were present in disease relapse and reappeared in five patients who had been in full remission. These results suggest that these hydroperoxidase-positive enlarged particles are pathognomonic of AML and that monitoring them with the light microscope may aid in guiding its clinical management.

Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies.

Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time-frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.

Using weighted linear spatial decomposition to investigate brain activity through a set of fixed current dipoles.

OBJECTIVES: We developed a method with the aim of decorrelating scalp EEG based on a set of spatial constraints. METHODS: We assume that the scalp EEG can be modelled by a small number of current dipoles of fixed location and orientation, placed at regions of interest. The algorithm is based on weighted linear spatial decomposition in order to obtain a weighted solution to the inverse problem. An EEG data matrix is first weighted in favour of a single dipole in the set. The dipole moment is then calculated from the weighted EEG by the pseudo-inverse method. This is repeated for each dipole. RESULTS: Six seizures were processed from 4 patients using the standard least-squares solution and our weighted version. The average cross-correlation between channels was calculated for each case. The first method resulted in a mean drop in cross-correlation of 16.5% from that of the scalp. Our method resulted in a reduction of 34.5%. CONCLUSIONS: Our method gives a more spatially decorrelated signal in regions of interest (although it is not intended as an accurate localization tool). Subsequent analysis is more robust and less likely to be dependent on specific recording montages. This is more than could be obtained using a standard least-squares solution using the same model.

The electroencephalogram through a software microscope: non-invasive localization and visualization of epileptic seizure activity from inside the brain.

OBJECTIVE: We developed a novel non-invasive analysis to localize the source and visualize the time course of electrical activity generated inside the brain but unclear from the scalp. This analysis applies to signals with unique waveform characteristics, such as seizures. METHODS: The method extracts activity from an EEG data matrix as a spatiotemporal component having waveforms uncorrelated to the other concurrent activities. The method also provides the location and orientation of the dipole generating this activity. We applied this method to ten scalp seizures in three patients with temporal lobe epilepsy and single-focus seizures confirmed by intracerebral recordings. A realistic head model based on MRI was used for computation of field distributions. RESULTS: When seizure activity was still not visually identifiable on the scalp, the method demonstrated in all scalp seizures a source in the temporal neocortex corresponding clearly to the region of seizure activity in intracerebral recordings. Frequency characteristics of the estimated activities also resembled those of the intracerebral seizures. CONCLUSIONS: This method enables estimation of focal brain activity when its effect on scalp EEG is unclear to visual examination. It works in situations where currently available source analyses methods, which require noiseless definite activity, are not applicable.

Isolation of epileptiform discharges from unaveraged EEG by independent component analysis.

OBJECTIVE: We propose a method that allows the separation of epileptiform discharges (EDs) from the EEG background, including the ED's waveform and spatial distribution. The method even allows to separate a spike in two components occurring at approximately the same time but having different waveforms and spatial distributions. METHODS: The separation employs independent component analysis (ICA) and is not based on any assumption regarding generator model. A simulation study was performed by generating ten EEG data matrices by computer: each matrix included real background activity from a normal subject to which was added an array of simulated unaveraged EDs. Each discharge was a summation of two transients having slightly different potential field distributions and small jitters in time and amplitude. Real EEG data were also obtained from three epileptic patients. RESULTS: Through ICA, we could isolate the two epileptiform transients in every simulation matrix, and the retrieved transients were almost identical as the originals, especially in their spatial distributions. Two epileptic components were isolated by ICA in all patients. Each estimated epileptic component had a consistent time course. CONCLUSION: ICA appears promising for the separation of unaveraged spikes from the EEG background and their decomposition in independent spatio-temporal components.

Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages.

OBJECTIVE: A multi-stage system for automated detection of epileptiform activity in the EEG has been developed and tested on pre-recorded data from 43 patients. METHODS: The system is centred on the use of an artificial neural network, known as the self-organising feature map (SOFM), as a novel pattern classifier. The role of the SOFM is to assign a probability value to incoming candidate epileptiform discharges (on a single channel basis). The multi-stage detection system consists of three major stages: mimetic, SOFM, and fuzzy logic. Fuzzy logic is introduced in order to incorporate spatial contextual information in the detection process. Through fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by the electroencephalographer. RESULTS: The system was trained on 35 epileptiform EEGs containing over 3000 epileptiform events and tested on a different set of eight EEGs containing 190 epileptiform events (including one normal EEG). Results show that the system has a sensitivity of 55.3% and a selectivity of 82% with a false detection rate of just over seven per hour. CONCLUSIONS: Based on these initial results the overall performance is favourable when compared with other leading systems in the literature. This encourages us to further test the system on a larger population base with the ultimate aim of introducing it into routine clinical use.

Effect of current level on electrode discrimination in electrical stimulation.

The effect of the stimulation intensity (current amplitude) on the ability to discriminate electrodes was tested in an experiment with four adult users of the Nucleus-22 cochlear implant. A total of 12 adjacent pairs of electrodes were used in the four-interval forced-choice discrimination task with random current variation. Tests were carried out at three average stimulation levels: 40 and 70% of the dynamic range and close to maximum comfortable loudness. Analysis of variance revealed a significant (P<0.0001) deterioration in electrode discrimination with a decreasing level. However, the overall effect was very small, representing a deterioration in the discrimination score of only 18% correct from the highest to lowest levels tested. The reason for the small deterioration in discriminability with a decreasing level is difficult to determine from this experiment, however, the results are consistent with the hypothesis that changes in the 'peak' or 'edge' of the excitation pattern are more important for discrimination tasks than the relative amount of non-overlap of the excitation areas from the two electrodes.

Multireference adaptive noise canceling applied to the EEG.

The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroeancephalogram (EEG), with the adaptation implemented by means of a multilayer-perception artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.

De-noising the abdominal phonogram for foetal heart rate extraction: blind source separation versus empirical filtering.

This work explored the suitability of using the foetal phonocardiogram (FPCG) blindly separated from the abdominal phonogram as a source for foetal heart rate (FHR) measuring in antenatal surveillance. To this end, and working on a dataset of 15 abdominal phonograms, the FPCG was estimated by using two de-noising approaches (1) single-channel independent component analysis (SCICA) to produce FPCG(e) and (2) empirical filtering to produce FPCG(g). Next, the FPCGs were further processed to collect the beat-to-beat FHR and the resulting time-series (FCTG(e) and FCTG(g) were compared to the reference signal given by the abdominal ECG (FCTG(r)). Results are promising, the FPCG(e) gives rise to a FCTG(e) that resembles FCTG(r) and, most importantly, whose mean FHR value is statistically equivalent to that given by FCTG(r) (p > 0.05). Thus, the mean FHR value obtained from the FPCG(e), is likely to be equivalent to the value given by the abdominal ECG, which is especially significant since the FPCG(e) is retrieved from the noisy abdominal phonogram. Hence, as far as this study has gone, it can be said that, when using SCICA to de-noise the abdominal phonogram, the resulting FPCG is likely to become a useful source for FHR collection in antenatal surveillance.

Antenatal surveillance through estimates of the sources underlying the abdominal phonogram: a preliminary study.

Today, it is generally accepted that current methods for biophysical antenatal surveillance do not facilitate a comprehensive and reliable assessment of foetal well-being and that continuing research into alternative methods is necessary to improve antenatal monitoring procedures. In our research, attention has been paid to the abdominal phonogram, a signal that is recorded by positioning an acoustic sensor on the maternal womb and contains valuable information about foetal status, but which is hidden by maternal and environmental sources. To recover such information, previous work has used single-channel independent component analysis (SCICA) on the abdominal phonogram and successfully retrieved estimates of the foetal phonocardiogram, the maternal phonocardiogram, the maternal respirogram and noise. The availability of these estimates made it possible for the current study to focus on their evaluation as sources for antenatal surveillance purposes. To this end, the foetal heart rate (FHR), the foetal heart sounds morphology, the maternal heart rate (MHR) and the maternal breathing rate (MBR) were collected from the estimates retrieved from a dataset of 25 abdominal phonograms. Next, these parameters were compared with reference values to quantify the significance of the physiological information extracted from the estimates. As a result, it has been seen that the instantaneous FHR, the instantaneous MHR and the MBR collected from the estimates consistently followed the trends given by the reference signals, which is a promising outcome for this preliminary study. Thus, as far as this study has gone, it can be said that the independent traces retrieved by SCICA from the abdominal phonogram are likely to become valuable sources of information for well-being surveillance, both foetal and maternal.

On the interpretation of the independent components underlying the abdominal phonogram: a study of their physiological relevance.

Recorded by positioning a sensitive acoustic sensor over the maternal womb, the abdominal phonogram is a signal that contains valuable information for foetal surveillance (e.g. heart rate), which is hidden by maternal and environmental sources. To recover such information, previous work used single-channel independent component analysis (SCICA) to separate the abdominal phonogram into statistically independent components (ICs) that, once acquired, must be objectively associated with the real sources underlying the abdominal phonogram-either physiological or environmental. This is a typical challenge for blind source separation methodologies and requires further research on the signals of interest to find a suitable solution. Here, we have conducted a joint study on 75 sets of ICs by means of statistical, spectral, complexity and time-structure analysis methods. As a result, valuable and consistent characteristics of the components separated from the abdominal phonogram by SCICA have been revealed: (1) the ICs are spectrally disjoint and sorted according to their frequency content, (2) only the ICs with lower frequency content present strong regular patterns and (3) such regular patterns are driven by well-known physiological processes given by the maternal breathing rate, the maternal heart rate and the foetal heart rate. This information was so promising that it has been used in current work for automatic classification of ICs and recovery of the traces of the physiological sources underlying the abdominal phonogram. Future work will look for the extraction of information useful for surveillance (e.g. heart rate), not only about foetal well-being, but also about maternal condition.

Extracting sources from noisy abdominal phonograms: a single-channel blind source separation method.

In this work we highlight a methodology that extracts sources from noisy single-channel abdominal phonograms. First, an appropriate matrix of delays is constructed. Next, multiple independent components are calculated using the FastICA algorithm. Then these components are projected back to the measurement space and classified for recovering the sources of interest. Single-channel phonograms obtained from three different subjects were analysed. Results show successful extraction of foetal heart sounds (FHS), maternal respiration/pulse wave, and line noise. It is important to point out the high performance of the method for extracting the former two as separate sources; especially due to the fact that pulse wave and FHS may overlap as maternal and foetal QRSs do in the abdominal ECG. The most outstanding factor is that this is achieved using a single-channel method. So, this approach extracts physiological sources from noisy abdominal phonograms, and we believe it will be useful for surveillance, not only for foetal well-being but also for maternal condition.

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.

Residual dependency estimation of independent components applied to EEG event related potentials associated with emotional processing.

Independent component analysis can be employed as an exploratory method in electroencephalographic (EEG) data analysis. However, the assumption of statistical independence among the estimated components is not always fulfilled by ICA-based numerical methods. Furthermore it may happen that one physiological source can be split in two or more components. As a consequence, the estimated components must be further investigated to assess the existence of reciprocal similarities. In this work a method for finding residual dependency subsets of component is proposed. Firstly a hierarchical clustering stage is carried out to classify ICA results. Then the hierarchical tree is investigated at each level by two indices to evaluate the tightness of all clusters. At the same time clustered scalp projections are compared with a template, which is shaped by applying ensemble ICA to a training dataset. Results are shown on EEG data acquired in event-related brain potentials (ERPs) studies for emotional pictures processing. In this kind of experiment ERPs are measured whilst unpleasant and neutral images are shown to a subject. The clustering procedure and the performance indices succeeded in isolating compact groups of components. These components, taken together, reflect the brain's biopotentials related to emotional processing at different cortical areas.