DeepLung: Smartphone Convolutional Neural Network-Based Inference of Lung Anomalies for Pulmonary Patients

Ahmed, Mahmuda; Rahman, Md. Mahbubur; Kuang, Jilong

doi:10.21437/interspeech.2019-2953

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…In ongoing work we are extending the dataset, hoping that this will allow more advanced classifiers, such as complex neural architectures, to perform better than the LR baseline [53,54]. We are also continuing our attempts to improve classification performance by including audio captured simultaneously from two stethoscope channels.…”

Section: Discussionmentioning

confidence: 99%

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Pahar,

Klopper,

Reeve

et al. 2021

Preprint

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We present first results showing that it is possible to automatically discriminate between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments in a real-world noisy environment. Our experiments are based on a dataset of cough recordings obtained in a real-world clinic setting from 16 patients confirmed to be suffering from TB and 33 patients that are suffering from respiratory conditions, confirmed as other than TB. We have trained and evaluated several machine learning classifiers, including logistic regression (LR), support vector machines (SVM), knearest neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks (CNN) inside a nested k-fold cross-validation and find that, although classification is possible in all cases, the best performance is achieved using the LR classifier. In combination with feature selection by sequential forward search (SFS), our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients (MFCCs). This system achieves a sensitivity of 93% at a specificity of 95% and thus exceeds the 90% sensitivity at 70% specificity specification considered by the WHO as minimal requirements for community-based TB triage test. We conclude that automatic classification of cough audio sounds is promising as a viable means of low-cost easily-deployable front-line screening for TB, which will greatly benefit developing countries with a heavy TB burden.

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Section: Discussionmentioning

confidence: 99%

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Pahar,

Klopper,

Reeve

et al. 2021

Preprint

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“… 70 A study inferred lung anomalies by applying a CNN model to audio and inertial sensor data from smartphones for pulmonary patients. 71 Depression was diagnosed by analyzing voice samples of PD patients with MLP, 74 which showed high prediction accuracy of 77% compared with other prediction models based on machine learning (0.62–0.76). Encoders, meanwhile, have been applied to predict morbidity and mortality.…”

Section: Deep Learning Methods For Prediction and Dimensionality Redu...mentioning

confidence: 99%

“…Ahmed et al 68 , Kim et al 69 , Srivastava et al 70 , Ahmed et al 71 Bidja 72 , Zhang et al 73 MLP 74 , Encoder 75 Integration of multimodal data Jung et al 76 Rutkowski et al 77 , Han et al 78 Dong et al 79 CNN and Encoder 30 , Transformer 80 , DNN (not specified) 81 Biosignals accompanying environmental information Kanjo et al 82 , Mou et al 83 He et al edge. After constructing the two GCN layers, an FC layer of softmax activation was combined.…”

Section: Activity Datamentioning

confidence: 99%

Applications of deep learning methods in digital biomarker research using noninvasive sensing data

Jeong

Park

et al. 2022

DIGITAL HEALTH

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Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.

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“…This approach is relatively non-intrusive, and can be completely non-contact, requiring only the presence of an acoustic sensing device within a reasonable proximity (primarily governed by the level of background noise) and a device to facilitate the requisite signal processing. Some implementations have demonstrated use of a contact microphone [ 162 , 163 , 164 , 165 ]; however, many modern approaches have adopted the ubiquitous smart-phone for audio capture (and in some instances, also processing) device [ 166 , 167 , 168 , 169 ]. A cough event lasts somewhere between 300 [ 170 ] and 650 ms [ 171 ], and can be described by three distinct phases: the expulsive phase, the intermediate phase, and the voiced phase [ 172 ].…”

Section: Acoustic Detection Of Respiratory Infectionmentioning

confidence: 99%

Noncontact Sensing of Contagion

Khanam

Chahl

Chahl³

et al. 2021

J. Imaging

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The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.

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DeepLung: Smartphone Convolutional Neural Network-Based Inference of Lung Anomalies for Pulmonary Patients

Cited by 6 publications

References 17 publications

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Applications of deep learning methods in digital biomarker research using noninvasive sensing data

Noncontact Sensing of Contagion

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