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Jeng FC, Matzdorf K, Hickman KL, Bauer SW, Carriero AE, McDonald K, Lin TH, Wang CY. Advancing Auditory Processing by Detecting Frequency-Following Responses Through a Specialized Machine Learning Model. Percept Mot Skills 2024; 131:417-431. [PMID: 38153030 DOI: 10.1177/00315125231225767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
In this study, we explore the feasibility and performance of detecting scalp-recorded frequency-following responses (FFRs) with a specialized machine learning (ML) model. By leveraging the strengths of feature extraction of the source separation non-negative matrix factorization (SSNMF) algorithm and its adeptness in handling limited training data, we adapted the SSNMF algorithm into a specialized ML model with a hybrid architecture to enhance FFR detection amidst background noise. We recruited 40 adults with normal hearing and evoked their scalp recorded FFRs using the English vowel/i/with a rising pitch contour. The model was trained on FFR-present and FFR-absent conditions, and its performance was evaluated using sensitivity, specificity, efficiency, false-positive rate, and false-negative rate metrics. This study revealed that the specialized SSNMF model achieved heightened sensitivity, specificity, and efficiency in detecting FFRs as the number of recording sweeps increased. Sensitivity exceeded 80% at 500 sweeps and maintained over 89% from 1000 sweeps onwards. Similarly, specificity and efficiency also improved rapidly with increasing sweeps. The progressively enhanced sensitivity, specificity, and efficiency of this specialized ML model underscore its practicality and potential for broader applications. These findings have immediate implications for FFR research and clinical use, while paving the way for further advancements in the assessment of auditory processing.
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Affiliation(s)
- Fuh-Cherng Jeng
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
- Communication Sciences and Disorders, Asia University, Taichung, Taiwan
| | - Katie Matzdorf
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | - Kassy L Hickman
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | - Sydney W Bauer
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | - Amanda E Carriero
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | - Kalyn McDonald
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | - Tzu-Hao Lin
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Ching-Yuan Wang
- Department of Otolaryngology-HNS, China Medical University Hospital, Taichung, Taiwan
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Giordano AT, Jeng FC, Black TR, Bauer SW, Carriero AE, McDonald K, Lin TH, Wang CY. Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization. Percept Mot Skills 2023; 130:1834-1851. [PMID: 37534595 DOI: 10.1177/00315125231191303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better (p < .05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.
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Affiliation(s)
- Allison T Giordano
- Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA
| | - Fuh-Cherng Jeng
- Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA
| | - Taylor R Black
- Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA
| | - Sydney W Bauer
- Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA
| | - Amanda E Carriero
- Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA
| | - Kalyn McDonald
- Communication Sciences and Disorders, Ohio University, Athens, Ohio, USA
| | - Tzu-Hao Lin
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Ching-Yuan Wang
- Department of Otolaryngology-HNS, China Medical University Hospital, Taichung, Taiwan
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Wimalarathna H, Ankmnal-Veeranna S, Allan C, Agrawal SK, Samarabandu J, Ladak HM, Allen P. Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107118. [PMID: 36122495 DOI: 10.1016/j.cmpb.2022.107118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/01/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how machine learning has been used to develop algorithms to assess auditory brainstem responses. A clear and comprehensive overview is provided to allow clinicians and researchers to explore the domain and the potential translation to clinical care. METHODS The systematic review was performed based on PRISMA guidelines. A search was conducted of PubMed, IEEE-Xplore, and Scopus databases focusing on human studies that have used machine learning to assess auditory brainstem responses. The duration of the search was from January 1, 1990, to April 3, 2021. The Covidence systematic review platform (www.covidence.org) was used throughout the process. RESULTS A total of 5812 studies were found through the database search and 451 duplicates were removed. The title and abstract screening process further reduced the article count to 89 and in the proceeding full-text screening, 34 articles met our full inclusion criteria. CONCLUSION Three categories of applications were found, namely neurologic diagnosis, hearing threshold estimation, and other (does not relate to neurologic or hearing threshold estimation). Neural networks and support vector machines were the most commonly used machine learning algorithms in all three categories. Only one study had conducted a clinical trial to evaluate the algorithm after development. Challenges remain in the amount of data required to train machine learning models. Suggestions for future research avenues are mentioned with recommended reporting methods for researchers.
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Affiliation(s)
- Hasitha Wimalarathna
- Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada; National Centre for Audiology, Western University, London, Ontario, Canada.
| | - Sangamanatha Ankmnal-Veeranna
- National Centre for Audiology, Western University, London, Ontario, Canada; College of Nursing and Health Professions, School of Speech and Hearing Sciences, The University of Southern Mississippi, J.B. George Building, Hattiesburg, MS, USA
| | - Chris Allan
- National Centre for Audiology, Western University, London, Ontario, Canada; School of Communication Sciences & Disorders, Western University, London, Ontario, Canada
| | - Sumit K Agrawal
- Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada; National Centre for Audiology, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Otolaryngology - Head and Neck Surgery, Western University, London, Ontario, Canada
| | - Jagath Samarabandu
- Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada
| | - Hanif M Ladak
- Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada; National Centre for Audiology, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Otolaryngology - Head and Neck Surgery, Western University, London, Ontario, Canada
| | - Prudence Allen
- National Centre for Audiology, Western University, London, Ontario, Canada; School of Communication Sciences & Disorders, Western University, London, Ontario, Canada
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Jeng FC, Jeng YS. Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial. Semin Hear 2022; 43:251-274. [PMID: 36313046 PMCID: PMC9605809 DOI: 10.1055/s-0042-1756219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The frequency-following response (FFR) provides enriched information on how acoustic stimuli are processed in the human brain. Based on recent studies, machine learning techniques have demonstrated great utility in modeling human FFRs. This tutorial focuses on the fundamental principles, algorithmic designs, and custom implementations of several supervised models (linear regression, logistic regression, k -nearest neighbors, support vector machines) and an unsupervised model ( k -means clustering). Other useful machine learning tools (Markov chains, dimensionality reduction, principal components analysis, nonnegative matrix factorization, and neural networks) are discussed as well. Each model's applicability and its pros and cons are explained. The choice of a suitable model is highly dependent on the research question, FFR recordings, target variables, extracted features, and their data types. To promote understanding, an example project implemented in Python is provided, which demonstrates practical usage of several of the discussed models on a sample dataset of six FFR features and a target response label.
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Affiliation(s)
- Fuh-Cherng Jeng
- Communication Sciences and Disorders, Ohio University, Athens, Ohio
| | - Yu-Shiang Jeng
- Computer Science and Engineering, Ohio State University, Columbus, Ohio
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Jeng FC, Lin TH, Hart BN, Montgomery-Reagan K, McDonald K. Non-negative matrix factorization improves the efficiency of recording frequency-following responses in normal-hearing adults and neonates. Int J Audiol 2022:1-11. [PMID: 35522832 DOI: 10.1080/14992027.2022.2071345] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE One challenge in extracting the scalp-recorded frequency-following response (FFR) is related to its inherently small amplitude, which means that the response cannot be identified with confidence when only a relatively small number of recording sweeps are included in the averaging procedure. DESIGN This study examined how the non-negative matrix factorisation (NMF) algorithm with a source separation constraint could be applied to improve the efficiency of FFR recordings. Conventional FFRs elicited by an English vowel/i/with a rising frequency contour were collected. Study sample: Fifteen normal-hearing adults and 15 normal-hearing neonates were recruited. RESULTS The improvements of FFR recordings, defined as the correlation coefficient and root-mean-square differences across a sweep series of amplitude spectrograms before and after the application of the source separation NMF (SSNMF) algorithm, were characterised through an exponential curve fitting model. Statistical analysis of variance indicated that the SSNMF algorithm was able to enhance the FFRs recorded in both groups of participants. CONCLUSIONS Such improvements enabled FFR extractions in a relatively small number of recording sweeps, and opened a new window to better understand how speech sounds are processed in the human brain.
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Affiliation(s)
- Fuh-Cherng Jeng
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | - Tzu-Hao Lin
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Breanna N Hart
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
| | | | - Kalyn McDonald
- Communication Sciences and Disorders, Ohio University, Athens, OH, USA
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