The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform
G. Cantarella - One of the best experts on this subject based on the ideXlab platform.
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a rare cause of extremely loud expiratory stridor in a 11 year old patient
Laryngoscope, 2021Co-Authors: G. Cantarella, Anna Clara CiabattaAbstract:Stridor can be a very alarming symptom in children, as it is typically caused by turbulent airflow through a partially obstructed airway, due to an organic cause. An Abnormal Respiratory Sound can induce the suspicion of a life-threatening clinical condition and requires an immediate diagnostic assessment. The aim of this article is to describe a very peculiar case of loud expiratory stridor, with an abrupt onset and no other associated symptoms, occurring in an 11-year-old girl. The stridor Sounds ceased only during sleep at night. Videolaryngoscopy demonstrated that the anatomy and patency of the larynx were normal, and expiratory noise was generated by vibration of the supraglottic structures. Voice therapy led to remission of stridor within 3 weeks. To the best of our knowledge, this is the first reported case of expiratory stridor with such striking volume and characteristics related to psychological causes. This peculiar clinical condition needs to be taken into consideration in the differential diagnosis of pediatric stridor to avoid unnecessary and invasive procedures and treatments. Laryngoscope, 131:E929-E931, 2021.
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A Rare Cause of Extremely Loud Expiratory Stridor in a 11‐Year‐Old Patient
'Wiley', 2020Co-Authors: G. Cantarella, Clara A. CiabattaAbstract:Stridor can be a very alarming symptom in children, as it is typically caused by turbulent airflow through a partially obstructed airway, due to an organic cause. An Abnormal Respiratory Sound can induce the suspicion of a life-threatening clinical condition and requires an immediate diagnostic assessment. The aim of this article is to describe a very peculiar case of loud expiratory stridor, with an abrupt onset and no other associated symptoms, occurring in an 11-year-old girl. The stridor Sounds ceased only during sleep at night. Videolaryngoscopy demonstrated that the anatomy and patency of the larynx were normal, and expiratory noise was generated by vibration of the supraglottic structures. Voice therapy led to remission of stridor within 3 weeks. To the best of our knowledge, this is the first reported case of expiratory stridor with such striking volume and characteristics related to psychological causes. This peculiar clinical condition needs to be taken into consideration in the differential diagnosis of pediatric stridor to avoid unnecessary and invasive procedures and treatments
Anna Clara Ciabatta - One of the best experts on this subject based on the ideXlab platform.
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a rare cause of extremely loud expiratory stridor in a 11 year old patient
Laryngoscope, 2021Co-Authors: G. Cantarella, Anna Clara CiabattaAbstract:Stridor can be a very alarming symptom in children, as it is typically caused by turbulent airflow through a partially obstructed airway, due to an organic cause. An Abnormal Respiratory Sound can induce the suspicion of a life-threatening clinical condition and requires an immediate diagnostic assessment. The aim of this article is to describe a very peculiar case of loud expiratory stridor, with an abrupt onset and no other associated symptoms, occurring in an 11-year-old girl. The stridor Sounds ceased only during sleep at night. Videolaryngoscopy demonstrated that the anatomy and patency of the larynx were normal, and expiratory noise was generated by vibration of the supraglottic structures. Voice therapy led to remission of stridor within 3 weeks. To the best of our knowledge, this is the first reported case of expiratory stridor with such striking volume and characteristics related to psychological causes. This peculiar clinical condition needs to be taken into consideration in the differential diagnosis of pediatric stridor to avoid unnecessary and invasive procedures and treatments. Laryngoscope, 131:E929-E931, 2021.
Sunil Kumar Kopparapu - One of the best experts on this subject based on the ideXlab platform.
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deep lung auscultation using acoustic biomarkers for Abnormal Respiratory Sound event detection
International Conference on Acoustics Speech and Signal Processing, 2021Co-Authors: Upasana Tiwari, Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar KopparapuAbstract:Lung Auscultation is a non-invasive process of distinguishing normal Respiratory Sounds from Abnormal ones by analyzing the airflow along the Respiratory tract. With developments in the Deep Learning (DL) techniques and wider access to anonymized medical data, automatic detection of specific Sounds such as crackles and wheezes have been gaining popularity. In this paper, we propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model trained using Sounds from daily activities. First set of biomarkers highlight the time frequency localization characteristics obtained from DWT coefficients. However, the second set of deep encoded biomarkers captures a generalized reliable representation, and thus indemnifies the scarcity of training samples and the class imbalance in dataset. The model trained using these features achieves a 15.05% increase in terms of the specificity over the baseline model that uses spectrogram features. Moreover, ensemble of DWT features and deep encoded feature based models show absolute improvements of 8.32%, 6.66% and 7.40% in terms of sensitivity, specificity and ICBHI-score, respectively, and clearly outperforms the state-of-the-art with a significant margin.
Upasana Tiwari - One of the best experts on this subject based on the ideXlab platform.
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deep lung auscultation using acoustic biomarkers for Abnormal Respiratory Sound event detection
International Conference on Acoustics Speech and Signal Processing, 2021Co-Authors: Upasana Tiwari, Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar KopparapuAbstract:Lung Auscultation is a non-invasive process of distinguishing normal Respiratory Sounds from Abnormal ones by analyzing the airflow along the Respiratory tract. With developments in the Deep Learning (DL) techniques and wider access to anonymized medical data, automatic detection of specific Sounds such as crackles and wheezes have been gaining popularity. In this paper, we propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model trained using Sounds from daily activities. First set of biomarkers highlight the time frequency localization characteristics obtained from DWT coefficients. However, the second set of deep encoded biomarkers captures a generalized reliable representation, and thus indemnifies the scarcity of training samples and the class imbalance in dataset. The model trained using these features achieves a 15.05% increase in terms of the specificity over the baseline model that uses spectrogram features. Moreover, ensemble of DWT features and deep encoded feature based models show absolute improvements of 8.32%, 6.66% and 7.40% in terms of sensitivity, specificity and ICBHI-score, respectively, and clearly outperforms the state-of-the-art with a significant margin.
Swapnil Bhosale - One of the best experts on this subject based on the ideXlab platform.
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deep lung auscultation using acoustic biomarkers for Abnormal Respiratory Sound event detection
International Conference on Acoustics Speech and Signal Processing, 2021Co-Authors: Upasana Tiwari, Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar KopparapuAbstract:Lung Auscultation is a non-invasive process of distinguishing normal Respiratory Sounds from Abnormal ones by analyzing the airflow along the Respiratory tract. With developments in the Deep Learning (DL) techniques and wider access to anonymized medical data, automatic detection of specific Sounds such as crackles and wheezes have been gaining popularity. In this paper, we propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model trained using Sounds from daily activities. First set of biomarkers highlight the time frequency localization characteristics obtained from DWT coefficients. However, the second set of deep encoded biomarkers captures a generalized reliable representation, and thus indemnifies the scarcity of training samples and the class imbalance in dataset. The model trained using these features achieves a 15.05% increase in terms of the specificity over the baseline model that uses spectrogram features. Moreover, ensemble of DWT features and deep encoded feature based models show absolute improvements of 8.32%, 6.66% and 7.40% in terms of sensitivity, specificity and ICBHI-score, respectively, and clearly outperforms the state-of-the-art with a significant margin.
