Diagnosis of pneumonia from sounds collected using low cost cell phones

Song, Insu

doi:10.1109/ijcnn.2015.7280317

“…Hence, audio analysis has been considered a potential means for lightweight diagnosis. There is work performing diagnosis with respiratory and lung sound analysis [170], which can work even with lowcost smartphones [171]. High mortality risk groups, including the elderly, can also be continuously monitored using speech analysis [172].…”

Section: Voice Sound Data Analysismentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

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<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

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“…Hence, audio analysis has been considered a potential means for lightweight diagnosis. There is work performing diagnosis with respiratory and lung sound analysis [156], which can work even with lowcost smartphones [157]. High mortality risk groups, including the elderly, can also be continuously monitored using speech analysis [158].…”

Section: Voice Sound Data Analysismentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

View full text Add to dashboard Cite

<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

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“…Acoustic features of respiration captured by the microphone placed near mouth and nose area were explored by Song to diagnose the pneumonia in children [50]. Using supervised learning with more than 1000 acoustic features (prosodic, spectral, cepstral features and their first and second-order coefficients) he obtained 92% accuracy for binary classification task: pneumonia vs. non-pneumonia [50].…”

Section: Measuring Respiration From the Audio Signalmentioning

confidence: 99%

The role of respiration audio in multimodal analysis of movement qualities

Lussu

¹

,

Niewiadomski

²

,

Volpe

³

et al. 2019

J Multimodal User Interfaces

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In this paper, we explore how the audio respiration signal can contribute to multimodal analysis of movement qualities. Within this aim, we propose two novel techniques which use the audio respiration signal captured by a standard microphone placed near to mouth and supervised machine learning algorithms. The first approach consists of the classification of a set of acoustic features extracted from exhalations of a person performing fluid or fragmented movements. In the second approach, the intrapersonal synchronization between the respiration and kinetic energy of body movements is used to distinguish the same qualities. First, the value of synchronization between modalities is computed using the Event Synchronization algorithm. Next, a set of features, computed from the value of synchronization, is used as an input to machine learning algorithms. Both approaches were applied to the multimodal corpus composed of short performances by three professionals performing fluid and fragmented movements. The total duration of the corpus is about 17 min. The highest F-score (0.87) for the first approach was obtained for the binary classification task using Support Vector Machines (SVM-LP). The best result for the same task using the second approach was obtained using Naive Bayes algorithm (F-score of 0.72). The results confirm that it is possible to infer information about the movement qualities from respiration audio.

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Diagnosis of pneumonia from sounds collected using low cost cell phones

Cited by 38 publications

References 25 publications

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

The role of respiration audio in multimodal analysis of movement qualities

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