Discriminant feature vectors for characterizing ailment cough vs. simulated cough

Jha, Ravi Shekhar; Singh, Vishwanath Pratap; Mittal, Vinay Kumar

doi:10.1109/tencon.2016.7848475

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Cough frequency can be associated with infection load and is relevant to disease transmission or conversion rates [ 13 ]. Changes in cough characteristics can reflect evolving pathological situations and can be used to monitor respiratory status in various pulmonary disorders [ 13 , 14 ]. With the recent advancements in the automatic detection of cough, increased amplitude, frequency, duration, severity, and pattern of cough are relevant features for the automatic detection of respiratory diseases [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report

Kang

Lee

Cheol-Ki

et al. 2021

Sensors

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Spirometer measurements can reflect cough strength but might not be routinely available for patients with severe neurological or medical conditions. A digital device that can record and help track abnormal cough sound changes serially in a noninvasive but reliable manner would be beneficial for monitoring such individuals. This report includes two cases of respiratory distress whose cough changes were monitored via assessments performed using recordings made with a digital device. The cough sounds were recorded using an iPad (Apple, Cupertino, CA, USA) through an embedded microphone. Cough sounds were recorded at the bedside, with no additional special equipment. The two patients were able to complete the recordings with no complications. The maximum root mean square values obtained from the cough sounds were significantly reduced when both cases were diagnosed with aspiration pneumonia. In contrast, higher values became apparent when the patients demonstrated a less severe status. Based on an analysis of our two cases, the patients’ cough sounds recorded with a commercial digital device show promise as potential digital biomarkers that may reflect aspiration risk related to attenuated cough force. Serial monitoring aided the decision making to resume oral feeding. Future studies should further explore the clinical utility of this technique.

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Section: Discussionmentioning

confidence: 99%

Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report

Kang

Lee

Cheol-Ki

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…There are two types of coughs: wet, mucus-productive cough and dry (nonproductive) cough. These have been repeatedly analyzed and characterized by phase duration or frequency for classification tasks [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

2022

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In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual’s health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets.

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“…Additionally, amplitude-related features, such as peak amplitude and energy distribution across different frequency bands (such as signal RMS -Root Mean Square, or ZCR -Zero Crossing Rate), are crucial in understanding the intensity and spectral content of the cough [52]. Frequency-domain features are another significant category, where cough sounds are transformed into the frequency spectrum [35], [53]. Fundamental frequency, dominant frequency, spectral centroid, as well as spectral bandwidth, slope, and skewness are among the frequency-domain features that provide information about the pitch and frequency characteristics of the cough sound [54].…”

Section: Cough Typesmentioning

confidence: 99%

Differentiation of Dry and Wet Cough Sounds using A Deep Learning Model and Data Augmentation

Huq

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Automated classification of cough sounds has increased in importance due to factors such as the worldwide COVID-19 pandemic. To train such classification models requires a large dataset of cough sounds; however, it remains challenging to find sufficient expert-labelled training data. This thesis explores a novel form of audio data augmentation, where training cough sounds are corrupted with varying levels of reverberation and Gaussian noise. The combination of noise and reverberation is more effective than both traditional image-based augmentation techniques and either noise or reverberation alone, leading to near-human accuracy on a wet vs. dry cough classification task using a ResNet18 model across two cough datasets. Alignment between the training and testing environments is examined using the Speech-Commands audio dataset. While models trained with the closest reverb and noise level to the test environment gave the best results, the proposed audio augmentation technique produces models with robust performance across test environments.Finally, I would like to thank my family -your unconditional love and encouragement has been the fuel that powered me through this experience. To my wife, Fariha, thank you for your love and patience, especially the last months of the program. To my sister and family, appi (Rumpa), cb (Tinku), Rehan & Faraz, thank you for always being there to cheer me up and encourage me every time I needed it. To my mother and father, thank you for your love, kindness, patience, and your belief in me.v Table of ContentsCopyright .

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Discriminant feature vectors for characterizing ailment cough vs. simulated cough

Cited by 8 publications

References 4 publications

Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report

Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

Differentiation of Dry and Wet Cough Sounds using A Deep Learning Model and Data Augmentation

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