Emotion recognition from multimodal physiological measurements based on an interpretable feature selection method.

Many studies in literature successfully use classification algorithms to classify emotions by means of physiological signals. However, there are still important limitations in interpretability of the results, i.e. lack of feature specific characterizations for each emotional state. To this extent, our study proposes a feature selection method that allows to determine the most informative subset of features extracted from physiological signals by maintaining their original dimensional space. Results show that features from the galvanic skin response are confirmed to be relevant in separating the arousal dimension, especially fear from happiness and relaxation. Furthermore, the average and the median value of the galvanic skin response signal together with the ratio between SD1 and SD2 from the Poincarè analysis of the electrocardiogram signal, were found to be the most important features for the discrimination along the valence dimension. A Linear Discriminant Analysis model using the first ten features sorted by importance, as defined by their ability to discriminate emotions with a bivariate approach, led to a three-class test accuracy in discriminating happiness, relaxation and fear equal to 72%, 67% and 89% respectively.Clinical relevance This study demonstrates the ability of physiological signals to assess the emotional state of different subjects, by providing a fast and efficient method to select most important indexes from the autonomic nervous system. The approach has high clinical relevance as it could be extended to assess other emotional states (e.g. stress and pain) characterizing pathological states such as post traumatic stress disorder and depression.

Evaluation of a Novel Speech-in-Noise Test for Hearing Screening: Classification Performance and Transducers' Characteristics.

One of the current gaps in teleaudiology is the lack of methods for adult hearing screening viable for use in individuals of unknown language and in varying environments. We have developed a novel automated speech-in-noise test that uses stimuli viable for use in non-native listeners. The test reliability has been demonstrated in laboratory settings and in uncontrolled environmental noise settings in previous studies. The aim of this study was: (i) to evaluate the ability of the test to identify hearing loss using multivariate logistic regression classifiers in a population of 148 unscreened adults and (ii) to evaluate the ear-level sound pressure levels generated by different earphones and headphones as a function of the test volume. The multivariate classifiers had sensitivity equal to 0.79 and specificity equal to 0.79 using both the full set of features extracted from the test as well as a subset of three features (speech recognition threshold, age, and number of correct responses). The analysis of the ear-level sound pressure levels showed substantial variability across transducer types and models, with earphones levels being up to 22 dB lower than those of headphones. Overall, these results suggest that the proposed approach might be viable for hearing screening in varying environments if an option to self-adjust the test volume is included and if headphones are used. Future research is needed to assess the viability of the test for screening at a distance, for example by addressing the influence of user interface, device, and settings, on a large sample of subjects with varying hearing loss.

An Automated Speech-in-Noise Test for Remote Testing: Development and Preliminary Evaluation.

Purpose The aim of this study was to develop and evaluate a novel, automated speech-in-noise test viable for widespread in situ and remote screening. Method Vowel-consonant-vowel sounds in a multiple-choice consonant discrimination task were used. Recordings from a professional male native English speaker were used. A novel adaptive staircase procedure was developed, based on the estimated intelligibility of stimuli rather than on theoretical binomial models. Test performance was assessed in a population of 26 young adults (YAs) with normal hearing and in 72 unscreened adults (UAs), including native and nonnative English listeners. Results The proposed test provided accurate estimates of the speech recognition threshold (SRT) compared to a conventional adaptive procedure. Consistent outcomes were observed in YAs in test/retest and in controlled/uncontrolled conditions and in UAs in native and nonnative listeners. The SRT increased with increasing age, hearing loss, and self-reported hearing handicap in UAs. Test duration was similar in YAs and UAs irrespective of age and hearing loss. The test-retest repeatability of SRTs was high (Pearson correlation coefficient = .84), and the pass/fail outcomes of the test were reliable in repeated measures (Cohen's κ = .8). The test was accurate in identifying ears with pure-tone thresholds > 25 dB HL (accuracy = 0.82). Conclusion This study demonstrated the viability of the proposed test in subjects of varying language in terms of accuracy, reliability, and short test time. Further research is needed to validate the test in a larger population across a wider range of languages and hearing loss and to identify optimal classification criteria for screening purposes.

Development and preliminary evaluation of a novel adaptive staircase procedure for automated speech-in-noise testing.

This article introduces a novel automated staircase procedure for a speech-in-noise test based on Vowel-Consonant-Vowel (VCV) stimuli. Conventional staircase procedures are based on pre-determined changes in stimulus presentation levels under the assumption of homogeneous set. The proposed staircase takes into account differences in intelligibility across the set by using changes in stimulus presentation levels that vary depending on the stimulus and the presentation level. Preliminary evaluation of the proposed staircase, as compared to a conventional staircase, demonstrated test-retest reliability, agreement with the conventional method in terms of speech-in-noise threshold estimation, and lower test duration. As such, the proposed approach shows promise for the implementation of rapid and reliable speech-in-noise tests in adults. Further research is needed to assess test performance in a larger sample of participants, also including subjects of various mother tongue and subjects with hearing loss.