The Experts below are selected from a list of 76893 Experts worldwide ranked by ideXlab platform
Anne B. Chang - One of the best experts on this subject based on the ideXlab platform.
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Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases
Annals of Biomedical Engineering, 2013Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. Chang, Amalia Setyati, Rina TriasihAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an Automated Technology to classify cough into ‘wet’ and ‘dry’ categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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Automated algorithm for wet dry cough sounds classification
International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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EMBC - Automated algorithm for Wet/Dry cough sounds classification
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
V Swarnkar - One of the best experts on this subject based on the ideXlab platform.
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Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases
Annals of Biomedical Engineering, 2013Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. Chang, Amalia Setyati, Rina TriasihAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an Automated Technology to classify cough into ‘wet’ and ‘dry’ categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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Automated algorithm for wet dry cough sounds classification
International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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EMBC - Automated algorithm for Wet/Dry cough sounds classification
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
Simo Authie - One of the best experts on this subject based on the ideXlab platform.
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the emerging role of in vitro electrophysiological methods in cns safety pharmacology
Journal of Pharmacological and Toxicological Methods, 2016Co-Authors: Michael V. Accardi, Michael K. Pugsley, Roy Forste, Eric Troncy, Hai Huang, Simo AuthieAbstract:Adverse CNS effects account for a sizeable proportion of all drug attrition cases. These adverse CNS effects are mediated predominately by off-target drug activity on neuronal ion-channels, receptors, transporters and enzymes - altering neuronal function and network communication. In response to these concerns, there is growing support within the pharmaceutical industry for the requirement to perform more comprehensive CNS safety testing prior to first-in-human trials. Accordingly, CNS safety pharmacology commonly integrates several in vitro assay methods for screening neuronal targets in order to properly assess therapeutic safety. One essential assay method is the in vitro electrophysiological technique - the 'gold standard' ion channel assay. The in vitro electrophysiological method is a useful technique, amenable to a variety of different tissues and cell configurations, capable of assessing minute changes in ion channel activity from the level of a single receptor to a complex neuronal network. Recent advances in Automated Technology have further expanded the usefulness of in vitro electrophysiological methods into the realm of high-throughput, addressing the bottleneck imposed by the manual conduct of the technique. However, despite a large range of applications, manual and Automated in vitro electrophysiological techniques have had a slow penetrance into the field of safety pharmacology. Nevertheless, developments in throughput capabilities and in vivo applicability have led to a renewed interest in in vitro electrophysiological techniques that, when complimented by more traditional safety pharmacology methods, often increase the preclinical predictability of potential CNS liabilities.
Yusuf A Amrulloh - One of the best experts on this subject based on the ideXlab platform.
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Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases
Annals of Biomedical Engineering, 2013Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. Chang, Amalia Setyati, Rina TriasihAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an Automated Technology to classify cough into ‘wet’ and ‘dry’ categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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Automated algorithm for wet dry cough sounds classification
International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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EMBC - Automated algorithm for Wet/Dry cough sounds classification
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
Udantha R. Abeyratne - One of the best experts on this subject based on the ideXlab platform.
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Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases
Annals of Biomedical Engineering, 2013Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. Chang, Amalia Setyati, Rina TriasihAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an Automated Technology to classify cough into ‘wet’ and ‘dry’ categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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Automated algorithm for wet dry cough sounds classification
International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
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EMBC - Automated algorithm for Wet/Dry cough sounds classification
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012Co-Authors: V Swarnkar, Yusuf A Amrulloh, Udantha R. Abeyratne, Anne B. ChangAbstract:Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully Automated Technology to classify cough into ‘Wet’ and ‘Dry’ categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
