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.

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.

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.

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.

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.

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.

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.

Blind source separation in single-channel EEG analysis: an application to BCI.

In this work we present a technique for applying Blind Source Separation (BSS) to single channel recordings of Electromagnetic (EM) brain signals. Single channel recordings of brain signals are preprocessed through the method of delays, and the delay matrix processed with the BSS technique described here called LSDIAGTD which uses temporal decorrelation to implement the now popular Independent Component Analysis (ICA) algorithm. This allows the identification and extraction of statistically independent sources underlying these single channel recordings. In particular we depict the analysis of single channel recordings from a Brain-Computer Interfacing paradigm. We show that BSS technique applied in this way extracts a series of codebook vectors representing the spectral content underlying the recorded signal. It then becomes possible to identify and extract particular rhythmic activity underlying the recordings. We show that rhythmic activity in the 8 to 12Hz band can be extracted in the case of imagined hand movements for a particular BCI paradigm.

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.

Biomedical engineering chapters and student societies: a UK and Republic of Ireland perspective.

Biomedical engineering is a thriving academic and industrial subject in the United Kingdom and Republic of Ireland (UKRI). We describe how the EMBS UKRI Chapter and EMB Student Society UKRI are trying to support this field. Geographical constraints and the smaller number of biomedical engineering undergraduate students mean that the Chapter and Student Society have been formed at a national level, rather than the conventional University-based clubs and societies typical in North America. The style of events is also slightly different, with a focus on conferences rather than short events lasting less than one day, offering increased value for delegates who have to travel significant distances to attend. The UKRI Chapter and Student Society are cooperating with related organizations in the UKRI, as well as drawing on various resources, to build on what has been achieved to date.

Stepwise model order estimation in blind source separation applied to ictal EEG.

Most algorithms for blind source separation (BSS) or independent component analysis (ICA) assume an equal number of sources as sensors. For multichannel electrophysiological recordings, such as the electroencephalogram (EEG), however, there are often far fewer sources of neurophysiologically relevant activity than the number of sensors. This adds a model order estimation problem to the source separation problem. Conventional estimates of the number of sources are based on the dominant eigenvalues of the data covariance matrix, obtained from principal component analysis (PCA), whose corresponding eigenvectors are also used for prewhitening. It is well known that PCA is susceptible to noise, leading to incorrect model order estimates and data distortion, which in turn limit the accuracy of the source estimates. It is therefore highly desirable to determine the correct number of sources and their spatial topographies directly, without PCA-based data truncation or prewhitening. In this work, we present a stepwise BSS method for extracting only the sources necessary for a sufficiently good least-square fit to the data. This simultaneously yields model order and source estimates, which we examine at different noise levels. We also show how only a few neurophysiologically meaningful components can be extracted from 25-channel ictal EEG.

Mapping scalp topographies of rhythmic EEG activity using temporal decorrelation based constrained ICA.

Independent component analysis (ICA) methods are being increasingly applied to the analysis of electromagnetic (EM) brain signals. However, these powerful techniques still generally require subjective a posteriori analysis in order to visualise neurophysiologically meaningful components in the outputs. Standard implementations of ICA are restrictive mainly due to the square mixing assumption (i.e., as many sources as measurement channels) - this is especially so with large multichannel recordings. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; as in tracking the changing scalp topographies of rhythmic activities. Through constraining the ICA solution it is possible to extract signals that are statistically independent, yet which are similar to some reference signal which incorporates the a priori information. We demonstrate this method on a multichannel recording of an epileptiform electroencephalogram (EEG), where we automate the repeated simultaneous extraction of both rhythmic seizure activity, as well as alpha-band activity, over an epoch of EEG. Subjective analysis of the results shows scalp topographies with realistic spatial distributions which conform to our neurophysiologic expectations. This work shows that constraining ICA can be a very useful technique, especially in automated systems and we demonstrate that this can be successfully applied to EM brain signal analysis.

Nitrite inhibits hydrogen production and kills the cattle parasite Tritrichomonas foetus.

AIMS: To investigate the effects of NaNO2 on the microaerophilic flagellated protozoan, Tritrichomonas foetus KV1, an economically important cattle parasite that inhabits the vagina and can spread rapidly through herds of animals by sexual transmission and leads to abortion of foetal calves. METHODS AND RESULTS: Growth of the parasite was inhibited by 50% in the presence of 4 mm NaNO2; immediate killing occurred at 10 mm. Mass spectrometric monitoring of gases showed that H2 and CO2 evolution were inhibited by NaNO2, and electron paramagnetic resonance spectrometry revealed a signal similar to that of a thiolate-iron-NO complex. Growth with sublethal concentrations of NaNO2 yielded organisms that produced ethanol rather than H2. CONCLUSIONS: NaNO2 probably inactivates FeS protein(s) of hydrogenosomes so as to inhibit the conversion of pyruvate (derived from maltose in the growth medium) to H2 and acetate. SIGNIFICANCE AND IMPACT OF THE STUDY: The use of NaNO2 as a topical antitrichomonal agent in veterinary practice is a possibility. At present, slaughter of infected animals is the favoured method of control.

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.

Monaural and binaural loudness measures in cochlear implant users with contralateral residual hearing.

OBJECTIVE: The aim was to measure the loudness of monaural and binaural stimuli in a group of cochlear implant users who had residual hearing in the nonimplanted ear, and to consider the implications of these measures for a binaural fitting consisting of a hearing aid and an implant in opposite ears. Three independent hypotheses were addressed: that the shapes of the electric and acoustic loudness growth functions would be similar, although the dynamic ranges would differ; that standard implant and hearing aid fittings would result in substantial loudness mismatches between the acoustic and electric signals; and that loudness summation would occur for binaural combinations of electric and acoustic signals. DESIGN: A modified version of the "Loudness Growth in 1/2-Octave Bands" method (Allen, Hall, & Jeng, 1990) was used to measure loudness growth for each ear of nine subjects. At the time of the experiment, the subject group included all implant users in Melbourne and Denver who were available for research and who also had sufficient residual hearing to use a hearing aid in the nonimplanted ear. Five acoustic frequencies and five electrodes were measured for each subject. The same subjects also estimated the loudness of a set of stimuli including monaural and binaural signals chosen to cover the loudness range from very soft to loud. RESULTS: The shapes of the averaged loudness growth functions were similar in impaired and electrically stimulated ears, although the shapes of iso-loudness curves were quite different in the two ears, and dynamic ranges varied considerably. Calculations based on the psychophysical data demonstrated that standard fitting procedures for cochlear implants and hearing aids lead to a complex pattern of loudness differences between the ears. A substantial amount of loudness summation was observed for the binaural stimuli, with most summation occurring when the acoustic and electric components were of equal loudness. This is consistent with observations for subjects with normal hearing and subjects with bilaterally impaired hearing. CONCLUSIONS: These experiments provide data on which criteria and methods for the binaural fitting of cochlear implants and hearing aids may be based. It is unlikely that standard monaural fitting methods for cochlear implants and hearing aids will result in balanced loudness between the two ears across a reasonably broad range of frequencies and levels. It is also likely that output levels of both devices will need to be reduced relative to a monaural fitting to compensate for the binaural summation of loudness in some listeners.

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.