scholarly journals Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning

2019 ◽  
Author(s):  
Md Sultan Mahmud ◽  
Faruk Ahmed ◽  
Rakib Al-Fahad ◽  
Kazi Ashraf Moinuddin ◽  
Mohammed Yeasin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Hearing Loss ◽  
Brain Regions ◽  
Evoked Response ◽  
Support Vector ◽  
Model Parameters ◽  
Stability Selection ◽  
Degraded Speech ◽  
Age Related

ABSTRACTSpeech comprehension in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of the cortex’s speech-evoked response that distinguish older adults with or without mild hearing loss. We used source montage to model scalp-recorded during a phoneme discrimination task conducted under clear and noise-degraded conditions. We applied machine learning analyses (stability selection and control) to choose features of the speech-evoked response that are consistent over a range of model parameters and support vector machine (SVM) classification to investigate the time course and brain regions that segregate groups and speech clarity. Whole-brain data analysis revealed a classification accuracy of 82.03% [area under the curve (AUC)=81.18%; F1-score 82.00%], distinguishing groups within ∼50 ms after speech onset (i.e., as early as the P1 wave).We observed lower accuracy of 78.39% [AUC=78.74%; F1-score=79.00%] and delayed classification performance when the speech token were embedded in noise, with group segregation at 60 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured over the RH. Moreover, stability selection analysis identified 13 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 15 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (76% accuracy). Our results identify two core neural networks associated with complex speech perception in older adults and confirm a larger number of neural regions, particularly in RH and frontal lobe, are active when processing degraded speech information.

scholarly journals Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5022
Author(s):  
Francesco Asci ◽  
Giovanni Costantini ◽  
Pietro Di Leo ◽  
Alessandro Zampogna ◽  
Giovanni Ruoppolo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Experimental Studies ◽  
Combined Effect ◽  
Support Vector ◽  
Ecological Value ◽  
Age Related ◽  
And Gender ◽  
Age Range ◽  
Learning Analysis

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

scholarly journals Age-related differences in the neural network dynamics underlying the predictability gain

2021 ◽  
Author(s):  
Anna Uta Rysop ◽  
Lea-Maria Schmitt ◽  
Jonas Obleser ◽  
Gesa Hartwigsen
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Speech Processing ◽  
Effective Connectivity ◽  
Network Dynamics ◽  
Brain Regions ◽  
Speech Comprehension ◽  
Inhibitory Influence ◽  
Degraded Speech ◽  
Age Related

AbstractSpeech comprehension is often challenged by increased background noise, but can be facilitated via the semantic context of a sentence. This predictability gain relies on an interplay of language-specific semantic and domain-general brain regions. However, age-related differences in the interactions within and between semantic and domain-general networks remain poorly understood. Here we investigated commonalities and differences in degraded speech processing in healthy young and old participants. Participants performed a sentence repetition task while listening to sentences with high and low predictable endings and varying intelligibility. Stimulus intelligibility was adjusted to individual hearing abilities. Older adults showed an undiminished behavioural predictability gain. Likewise, both groups recruited a similar set of semantic and cingulo-opercular brain regions. However, we observed age-related differences in effective connectivity for high predictable speech of increasing intelligibility. Young adults exhibited stronger coupling within the cingulo-opercular network and between a cingulo-opercular and a posterior temporal semantic node. Moreover, these interactions were excitatory in young adults but inhibitory in old adults. Finally, the degree of the inhibitory influence between cingulo-opercular regions was predictive of the behavioural sensitivity towards changes in intelligibility for high predictable sentences in older adults only. Our results demonstrate that the predictability gain is relatively preserved in older adults when stimulus intelligibility is individually adjusted. While young and old participants recruit similar brain regions, differences manifest in network dynamics. Together, these results suggest that ageing affects the network configuration rather than regional activity during successful speech comprehension under challenging listening conditions.

scholarly journals Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246397
Author(s):  
Keisuke Hirata ◽  
Makoto Suzuki ◽  
Naoki Iso ◽  
Takuhiro Okabe ◽  
Hiroshi Goto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Knee Extension ◽  
The Body ◽  
Functional Mobility ◽  
Support Vector ◽  
Timed Up And Go ◽  
Explanatory Variables ◽  
Machine Learning Classification ◽  
Age Related

Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body function domain) was the most closely related assessment. Naturally, the classification accuracy rate increased with a larger number of assessments as explanatory variables. However, knee extension torque remained the highest of all assessments. This extended to all combinations (of 2–3 assessments) that included knee extension torque. This suggests that resistance training may help protect individuals suffering from age-related declines in functional mobility.

Inefficient Encoding as an Explanation for Age-Related Deficits in Recollection-Based Processing

2014 ◽  
Vol 28 (3) ◽  
pp. 148-161 ◽  
Author(s):  
David Friedman ◽  
Ray Johnson
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Semantic Retrieval ◽  
Age Related ◽  
The Face ◽  
Episodic Memories

A cardinal feature of aging is a decline in episodic memory (EM). Nevertheless, there is evidence that some older adults may be able to “compensate” for failures in recollection-based processing by recruiting brain regions and cognitive processes not normally recruited by the young. We review the evidence suggesting that age-related declines in EM performance and recollection-related brain activity (left-parietal EM effect; LPEM) are due to altered processing at encoding. We describe results from our laboratory on differences in encoding- and retrieval-related activity between young and older adults. We then show that, relative to the young, in older adults brain activity at encoding is reduced over a brain region believed to be crucial for successful semantic elaboration in a 400–1,400-ms interval (left inferior prefrontal cortex, LIPFC; Johnson, Nessler, & Friedman, 2013 ; Nessler, Friedman, Johnson, & Bersick, 2007 ; Nessler, Johnson, Bersick, & Friedman, 2006 ). This reduced brain activity is associated with diminished subsequent recognition-memory performance and the LPEM at retrieval. We provide evidence for this premise by demonstrating that disrupting encoding-related processes during this 400–1,400-ms interval in young adults affords causal support for the hypothesis that the reduction over LIPFC during encoding produces the hallmarks of an age-related EM deficit: normal semantic retrieval at encoding, reduced subsequent episodic recognition accuracy, free recall, and the LPEM. Finally, we show that the reduced LPEM in young adults is associated with “additional” brain activity over similar brain areas as those activated when older adults show deficient retrieval. Hence, rather than supporting the compensation hypothesis, these data are more consistent with the scaffolding hypothesis, in which the recruitment of additional cognitive processes is an adaptive response across the life span in the face of momentary increases in task demand due to poorly-encoded episodic memories.

scholarly journals Contributions and limitations of using machine learning to predict noise-induced hearing loss

2021 ◽  
Author(s):  
Feifan Chen ◽  
Zuwei Cao ◽  
Emad M. Grais ◽  
Fei Zhao
Keyword(s):  
Machine Learning ◽  
Hearing Loss ◽  
Roc Curve ◽  
Prediction Error ◽  
Noise Exposure ◽  
Model Construction ◽  
Support Vector ◽  
Exposure Level ◽  
Noise Induced Hearing Loss ◽  
Current Review

Abstract Purpose Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction. Methods The authors searched PubMed, EMBASE and Scopus on November 26, 2020. Results Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve. Conclusion In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.

scholarly journals Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

2021 ◽  
Author(s):  
Adeline Jabès ◽  
Giuliana Klencklen ◽  
Paolo Ruggeri ◽  
Christoph M. Michel ◽  
Pamela Banta Lavenex ◽  
...  
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Resting State ◽  
Cognitive Aging ◽  
Temporal Dynamics ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Age Related

AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

scholarly journals Dual Effects of Hearing Loss and Dementia on Functional Limitations in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 214-214
Author(s):  
Michael McKee ◽  
Yunshu Zhou ◽  
Joshua Ehrlich ◽  
Elham Mahmoudi ◽  
Jennifer Deal ◽  
...  
Keyword(s):  
Older Adults ◽  
Hearing Loss ◽  
Self Care ◽  
Household Activity ◽  
Cross Sectional ◽  
Age Related ◽  
Increased Risk ◽  
Lower Mobility ◽  
Independent Effects ◽  
The Impact

Abstract Age-related hearing loss (HL) is both common and associated with elevated risk for cognitive decline and poorer health. To care for an aging population, it is critical to understand the effect of coexisting HL and dementia on functional activities. The effect of co-existing dementia and self-reported HL on daily functioning were assessed. A cross-sectional analysis was performed using nationally-representative data from the 2015 National Health and Aging Trends Study consisting of U.S. adults 65+. The sample included 1,829 adults with HL (22.8%) and 5,338 adults without HL. Multivariable Poisson regression was used to model the independent effects and interaction of self-reported HL and dementia status on three validated functional activity scales (self-care, mobility, and household). All analyses adjusted for sociodemographic and medical factors. HL participants were more likely to be white, older, male, less educated (p <0.01). 8.4% had possible dementia and 6.5% had probable dementia. Respondents with HL or possible or probable dementia had significantly lower mobility, self-care, and household activity scores (p<.001 for all comparisons) compared to their peers. A small yet significant interaction was present in all models, suggesting that HL respondents with co-occurring dementia had lower mobility, self-care, and household activity scores than predicted by the independent effects of dementia and self-reported HL (p<.001 for all comparisons). Older adults with co-occurring dementia and HL are at increased risk for poor functioning and should be screened by healthcare providers. Future work should consider the impact of intervention in this vulnerable/at-risk population.

scholarly journals Dynamic Detection of Delayed Cerebral Ischemia Using Machine Learning

2020 ◽  
Author(s):  
Murad Megjhani ◽  
Kalijah Terilli ◽  
Ayham Alkhachroum ◽  
David J. Roh ◽  
Sachin Agarwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Ischemia ◽  
Characteristic Curve ◽  
Delayed Cerebral Ischemia ◽  
Risk Scores ◽  
Support Vector ◽  
Model Parameters ◽  
Learning Approaches ◽  
Physiologic Data ◽  
Over Time

AbstractObjectiveTo develop a machine learning based tool, using routine vital signs, to assess delayed cerebral ischemia (DCI) risk over time.MethodsIn this retrospective analysis, physiologic data for 540 consecutive acute subarachnoid hemorrhage patients were collected and annotated as part of a prospective observational cohort study between May 2006 and December 2014. Patients were excluded if (i) no physiologic data was available, (ii) they expired prior to the DCI onset window (< post bleed day 3) or (iii) early angiographic vasospasm was detected on admitting angiogram. DCI was prospectively labeled by consensus of treating physicians. Occurrence of DCI was classified using various machine learning approaches including logistic regression, random forest, support vector machine (linear and kernel), and an ensemble classifier, trained on vitals and subject characteristic features. Hourly risk scores were generated as the posterior probability at time t. We performed five-fold nested cross validation to tune the model parameters and to report the accuracy. All classifiers were evaluated for good discrimination using the area under the receiver operating characteristic curve (AU-ROC) and confusion matrices.ResultsOf 310 patients included in our final analysis, 101 (32.6%) patients developed DCI. We achieved maximal classification of 0.81 [0.75-0.82] AU-ROC. We also predicted 74.7 % of all DCI events 12 hours before typical clinical detection with a ratio of 3 true alerts for every 2 false alerts.ConclusionA data-driven machine learning based detection tool offered hourly assessments of DCI risk and incorporated new physiologic information over time.

scholarly journals Machine Learning Models for Predicting Hearing Prognosis in Unilateral Idiopathic Sudden Sensorineural Hearing Loss

2020 ◽  
Vol 13 (2) ◽  
pp. 148-156
Author(s):  
Keon Vin Park ◽  
Kyoung Ho Oh ◽  
Yong Jun Jeong ◽  
Jihye Rhee ◽  
Mun Soo Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Sudden Sensorineural Hearing Loss ◽  
Sensorineural Hearing ◽  
Statistical Hypothesis ◽  
Support Vector ◽  
Learning Models ◽  
Svm Model ◽  
Machine Learning Models

Objectives. Prognosticating idiopathic sudden sensorineural hearing loss (ISSNHL) is an important challenge. In our study, a dataset was split into training and test sets and cross-validation was implemented on the training set, thereby determining the hyperparameters for machine learning models with high test accuracy and low bias. The effectiveness of the following five machine learning models for predicting the hearing prognosis in patients with ISSNHL after 1 month of treatment was assessed: adaptive boosting, K-nearest neighbor, multilayer perceptron, random forest (RF), and support vector machine (SVM).Methods. The medical records of 523 patients with ISSNHL admitted to Korea University Ansan Hospital between January 2010 and October 2017 were retrospectively reviewed. In this study, we analyzed data from 227 patients (recovery, 106; no recovery, 121) after excluding those with missing data. To determine risk factors, statistical hypothesis tests (e.g., the two-sample <i>t</i>-test for continuous variables and the chi-square test for categorical variables) were conducted to compare patients who did or did not recover. Variables were selected using an RF model depending on two criteria (mean decreases in the Gini index and accuracy).Results. The SVM model using selected predictors achieved both the highest accuracy (75.36%) and the highest F-score (0.74) on the test set. The RF model with selected variables demonstrated the second-highest accuracy (73.91%) and F-score (0.74). The RF model with the original variables showed the same accuracy (73.91%) as that of the RF model with selected variables, but a lower F-score (0.73). All the tested models, except RF, demonstrated better performance after variable selection based on RF.Conclusion. The SVM model with selected predictors was the best-performing of the tested prediction models. The RF model with selected predictors was the second-best model. Therefore, machine learning models can be used to predict hearing recovery in patients with ISSNHL.

scholarly journals Interprofessional Education Activity to Improve Communication With Older Adults With Hearing Loss

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 4-4
Author(s):  
Laura Gaeta ◽  
Tara Sharpp
Keyword(s):  
Older Adults ◽  
Hearing Loss ◽  
Nursing Students ◽  
Interprofessional Education ◽  
Complex Case ◽  
Evaluation Data ◽  
Age Related ◽  
Before And After ◽  
Education Activity ◽  
Age Related Hearing Loss

Abstract The purpose of this study was to determine how an interprofessional education (IPE) intervention with Communication Sciences and Disorders (CSAD) and Nursing students can affect their ability to communicate effectively with older adults who have hearing loss. As the older adult demographic increases, healthcare professionals must provide competent care, which includes effectively managing hearing-related communication difficulties in an increasingly diverse population. Faculty received IRB approval to conduct a descriptive mixed-methods study to determine knowledge and satisfaction of students completing an IPE activity. Students were divided into teams of CSAD and Nursing students. Students listened to a brief presentation on IPE before they were introduced to a complex case study of an 84-year-old male with age-related hearing loss. We administered a knowledge assessment questionnaire (KAQ) we created regarding communication with older adults before and after the activity. A total of 92 participants in the two programs (n=36 CSAD, n=56 Nursing) completed the KAQ before and after the activity and an evaluation with a Likert-type scale and open-ended questions. CSAD students scored significantly higher than Nursing students on the KAQ at baseline (F=25.69, p&lt;0.001) and KAQ scores increased significantly (F=57.04, p&lt;0.001) among both groups from pretest to posttest. The evaluation data indicated students were able to learn other perspectives and found the experience valuable. Based on the improvement in scores on the KAQ and evaluation data, this IPE activity increased knowledge related to communication with older adults with hearing loss and awareness of the roles of other professions.

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