The Experts below are selected from a list of 4059 Experts worldwide ranked by ideXlab platform
Christian Guilleminault - One of the best experts on this subject based on the ideXlab platform.
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acoustic assessment of snoring sound intensity in 1 139 individuals undergoing polysomnography
2017Co-Authors: Kent Wilson, Riccardo A. Stoohs, Thomas F. Mulrooney, Linda J Johnson, Christian GuilleminaultAbstract:Study objectives: To quantify the snoring sound intensity levels generated by individuals during polysomnographic testing and to examine the relationships between acoustic, polysomnographic, and clinical variables. Design: The prospective acquisition of acoustic and polysomnographic data with a retrospective medical chart review. Setting: A sleep laboratory at a primary care hospital. Participants: All 1,139 of the patients referred to the sleep laboratory for polysomnographic testing from 1980 to 1994. Interventions: The acoustic measurement of snoring sound intensity during sleep concurrent with polysomnographic testing. Measurements and results: Four Decibel levels were derived from snoring sound intensity recordings. L1 ,L 5, and L10 are measures of the sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear exceeded, respectively, for 1, 5, and 10% of the test period. The Leq is a measure of the A-weighted average intensity of a fluctuating acoustic signal over the total test period. L10 levels above 55 dBA were exceeded by 12.3% of the patients. The average levels of snoring sound intensity were significantly higher for men than for women. The levels of snoring sound intensity were associated significantly with the following: polysomnographic testing results, including the respiratory disturbance index (RDI), sleep latency, and the percentage of slow-wave sleep; demographic factors, including gender and body mass; and clinical factors, including snoring history, hypersomnolence, and breathing stoppage. Men with a body mass index of > 30 and an average snoring sound intensity of > 38 dBA were 4.1 times more likely to have an RDI of > 10. Conclusions: Snoring sound intensity levels are related to a number of demographic, clinical, and polysomnographic test results. Snoring sound intensity is closely related to apnea/hypopnea during sleep. The noise generated by snoring can disturb or disrupt a snorer’s sleep, as well as the sleep of a bed partner. (CHEST 1999; 115:762‐770) Abbreviations: BMI 5 body mass index; CI 5 confidence interval; dB 5 Decibel; dBA 5 sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear; L1 5 measure of the dBA level exceeded for 1% of the test period; L5 5 measure of the dBA level exceeded for 5% of the test period; L10 5 measure of the dBA level exceeded for 10% of the test period; Leq 5 a measurement of the A-weighted average energy of a fluctuating acoustic signal over a specific measurement period; LS 5 light sleep; MPCA 5 Minnesota Pollution Control Agency; OSHA 5 Occupational Safety and Health Administration; RDI 5 respiratory disturbance index; SWS 5 slow wave sleep
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The Snoring Spectrum* Acoustic Assessment of Snoring Sound Intensity in 1,139 Individuals Undergoing Polysomnography
2014Co-Authors: Christian Guilleminault, Zhen HuangAbstract:Study objectives: To quantify the snoring sound intensity levels generated by individuals during polysomnographic testing and to examine the relationships between acoustic, polysomnographic, and clinical variables. Design: The prospective acquisition of acoustic and polysomnographic data with a retrospective medical chart review. Setting: A sleep laboratory at a primary care hospital. Participants: All 1,139 of the patients referred to the sleep laboratory for polysomnographic testing from 1980 to 1994. Interventions: The acoustic measurement of snoring sound intensity during sleep concurrent with polysomnographic testing. Measurements and results: Four Decibel levels were derived from snoring sound intensity recordings. L1, L5, and L10 are measures of the sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear exceeded, respectively, for 1, 5, and 10 % of the test period. The Leq is a measure of the A-weighted average intensity of a fluctuating acoustic signal over the total test period. L10 levels above 55 dBA were exceeded by 12.3 % of th
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The snoring spectrum: Acoustic assessment of snoring sound intensity in 1,139 individuals undergoing polysomnography
Chest, 1999Co-Authors: Kent Wilson, Riccardo A. Stoohs, Thomas F. Mulrooney, Linda J Johnson, Christian GuilleminaultAbstract:Study objectives: To quantify the snoring sound intensity levels generated by individuals during polysomnographic testing and to examine the relationships between acoustic, polysomnographic, and clinical variables. Design: The prospective acquisition of acoustic and polysomnographic data with a retrospective medical chart review. Setting: A sleep laboratory at a primary care hospital. Participants: All 1,139 of the patients referred to the sleep laboratory for polysomnographic testing from 1980 to 1994. Interventions: The acoustic measurement of snoring sound intensity during sleep concurrent with polysomnographic testing. Measurements and results: Four Decibel levels were derived from snoring sound intensity recordings. L-1, L-5, and L-10 are measures of the sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear exceeded, respectively, for 1, 5, and 10% of the test period. The Leg is a measure of the A-weighted average intensity of a fluctuating; acoustic signal over the total test period. L-10 levels above 55 dBA were exceeded by 12.3% of the patients. The average levels of snoring sound intensity were significantly higher for men than for women. The levels of snoring sound intensity were associated significantly with the following: polysomnographic testing results, including the respiratory disturbance index (RDI), sleep latency, and the percentage of slow-wave sleep; demographic factors, including gender and body mass; and clinical factors, including snoring history, hypersomnolence, and breathing stoppage, Men with a body mass index of > 30 and an average snoring sound intensity of > 38 dBA were 4.1 times more likely to have an RDI of > 10. Conclusions: Snoring sound intensity levels are related to a number of demographic, clinical, and polysomnographic test results, Snoring sound intensity is closely related to apnea/hypopnea during sleep. The noise generated by snoring can disturb or disrupt a snorer's sleep, as wed as the sleep of a bed partner.
Tom Francart - One of the best experts on this subject based on the ideXlab platform.
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predicting individual speech intelligibility from the cortical tracking of acoustic and phonetic level speech representations
Hearing Research, 2019Co-Authors: Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Lien Decruy, Tom FrancartAbstract:Abstract Objective To objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each model's prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results The model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 Decibel for 53% and below 2 Decibels for 89% of the subjects. Conclusion A model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system.
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Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations
bioRxiv, 2018Co-Authors: Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Lien Decruy, Tom FrancartAbstract:ABSTRACT Objective To objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each model’s prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results The model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 Decibel for 53% and below 2 Decibels for 89% of the subjects. Conclusion A model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system. Search Terms cortical speech tracking, objective measure, speech intelligibility, auditory processing, speech representations. Highlights Objective EEG-based measure of speech intelligibility Improved prediction of speech intelligibility by combining speech representations Cortical tracking of speech in the delta EEG band monotonically increased with SNRs Cortical responses in the theta EEG band best predicted the speech reception threshold Disclosure The authors report no disclosures relevant to the manuscript.
Damien Lesenfants - One of the best experts on this subject based on the ideXlab platform.
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predicting individual speech intelligibility from the cortical tracking of acoustic and phonetic level speech representations
Hearing Research, 2019Co-Authors: Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Lien Decruy, Tom FrancartAbstract:Abstract Objective To objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each model's prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results The model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 Decibel for 53% and below 2 Decibels for 89% of the subjects. Conclusion A model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system.
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Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations
bioRxiv, 2018Co-Authors: Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Lien Decruy, Tom FrancartAbstract:ABSTRACT Objective To objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each model’s prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results The model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 Decibel for 53% and below 2 Decibels for 89% of the subjects. Conclusion A model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system. Search Terms cortical speech tracking, objective measure, speech intelligibility, auditory processing, speech representations. Highlights Objective EEG-based measure of speech intelligibility Improved prediction of speech intelligibility by combining speech representations Cortical tracking of speech in the delta EEG band monotonically increased with SNRs Cortical responses in the theta EEG band best predicted the speech reception threshold Disclosure The authors report no disclosures relevant to the manuscript.
Kent Wilson - One of the best experts on this subject based on the ideXlab platform.
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acoustic assessment of snoring sound intensity in 1 139 individuals undergoing polysomnography
2017Co-Authors: Kent Wilson, Riccardo A. Stoohs, Thomas F. Mulrooney, Linda J Johnson, Christian GuilleminaultAbstract:Study objectives: To quantify the snoring sound intensity levels generated by individuals during polysomnographic testing and to examine the relationships between acoustic, polysomnographic, and clinical variables. Design: The prospective acquisition of acoustic and polysomnographic data with a retrospective medical chart review. Setting: A sleep laboratory at a primary care hospital. Participants: All 1,139 of the patients referred to the sleep laboratory for polysomnographic testing from 1980 to 1994. Interventions: The acoustic measurement of snoring sound intensity during sleep concurrent with polysomnographic testing. Measurements and results: Four Decibel levels were derived from snoring sound intensity recordings. L1 ,L 5, and L10 are measures of the sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear exceeded, respectively, for 1, 5, and 10% of the test period. The Leq is a measure of the A-weighted average intensity of a fluctuating acoustic signal over the total test period. L10 levels above 55 dBA were exceeded by 12.3% of the patients. The average levels of snoring sound intensity were significantly higher for men than for women. The levels of snoring sound intensity were associated significantly with the following: polysomnographic testing results, including the respiratory disturbance index (RDI), sleep latency, and the percentage of slow-wave sleep; demographic factors, including gender and body mass; and clinical factors, including snoring history, hypersomnolence, and breathing stoppage. Men with a body mass index of > 30 and an average snoring sound intensity of > 38 dBA were 4.1 times more likely to have an RDI of > 10. Conclusions: Snoring sound intensity levels are related to a number of demographic, clinical, and polysomnographic test results. Snoring sound intensity is closely related to apnea/hypopnea during sleep. The noise generated by snoring can disturb or disrupt a snorer’s sleep, as well as the sleep of a bed partner. (CHEST 1999; 115:762‐770) Abbreviations: BMI 5 body mass index; CI 5 confidence interval; dB 5 Decibel; dBA 5 sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear; L1 5 measure of the dBA level exceeded for 1% of the test period; L5 5 measure of the dBA level exceeded for 5% of the test period; L10 5 measure of the dBA level exceeded for 10% of the test period; Leq 5 a measurement of the A-weighted average energy of a fluctuating acoustic signal over a specific measurement period; LS 5 light sleep; MPCA 5 Minnesota Pollution Control Agency; OSHA 5 Occupational Safety and Health Administration; RDI 5 respiratory disturbance index; SWS 5 slow wave sleep
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The snoring spectrum: Acoustic assessment of snoring sound intensity in 1,139 individuals undergoing polysomnography
Chest, 1999Co-Authors: Kent Wilson, Riccardo A. Stoohs, Thomas F. Mulrooney, Linda J Johnson, Christian GuilleminaultAbstract:Study objectives: To quantify the snoring sound intensity levels generated by individuals during polysomnographic testing and to examine the relationships between acoustic, polysomnographic, and clinical variables. Design: The prospective acquisition of acoustic and polysomnographic data with a retrospective medical chart review. Setting: A sleep laboratory at a primary care hospital. Participants: All 1,139 of the patients referred to the sleep laboratory for polysomnographic testing from 1980 to 1994. Interventions: The acoustic measurement of snoring sound intensity during sleep concurrent with polysomnographic testing. Measurements and results: Four Decibel levels were derived from snoring sound intensity recordings. L-1, L-5, and L-10 are measures of the sound pressure measurement in Decibels employing the A-weighting network that yields the response of the human ear exceeded, respectively, for 1, 5, and 10% of the test period. The Leg is a measure of the A-weighted average intensity of a fluctuating; acoustic signal over the total test period. L-10 levels above 55 dBA were exceeded by 12.3% of the patients. The average levels of snoring sound intensity were significantly higher for men than for women. The levels of snoring sound intensity were associated significantly with the following: polysomnographic testing results, including the respiratory disturbance index (RDI), sleep latency, and the percentage of slow-wave sleep; demographic factors, including gender and body mass; and clinical factors, including snoring history, hypersomnolence, and breathing stoppage, Men with a body mass index of > 30 and an average snoring sound intensity of > 38 dBA were 4.1 times more likely to have an RDI of > 10. Conclusions: Snoring sound intensity levels are related to a number of demographic, clinical, and polysomnographic test results, Snoring sound intensity is closely related to apnea/hypopnea during sleep. The noise generated by snoring can disturb or disrupt a snorer's sleep, as wed as the sleep of a bed partner.
Eline Verschueren - One of the best experts on this subject based on the ideXlab platform.
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predicting individual speech intelligibility from the cortical tracking of acoustic and phonetic level speech representations
Hearing Research, 2019Co-Authors: Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Lien Decruy, Tom FrancartAbstract:Abstract Objective To objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each model's prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results The model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 Decibel for 53% and below 2 Decibels for 89% of the subjects. Conclusion A model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system.
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Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations
bioRxiv, 2018Co-Authors: Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Lien Decruy, Tom FrancartAbstract:ABSTRACT Objective To objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each model’s prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results The model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 Decibel for 53% and below 2 Decibels for 89% of the subjects. Conclusion A model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system. Search Terms cortical speech tracking, objective measure, speech intelligibility, auditory processing, speech representations. Highlights Objective EEG-based measure of speech intelligibility Improved prediction of speech intelligibility by combining speech representations Cortical tracking of speech in the delta EEG band monotonically increased with SNRs Cortical responses in the theta EEG band best predicted the speech reception threshold Disclosure The authors report no disclosures relevant to the manuscript.
