CoughNet: A Flexible Low Power CNN-LSTM Processor for Cough Sound Detection

Rashid, Hasib-Al; Mazumder, Arnab Neelim; Niyogi, Utteja Panchakshara Kallakuri

doi:10.1109/aicas51828.2021.9458509

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…RNN can also be jointly applied with CNN model to better capture spatial-temporal features for respiratory sound classification. 60,61 Similar to RNN, another sequential modeling architecture is Transformer, which has been explored recently for cough-based COVID-19 detection. 37,41,56,62,79 79 Although most studies focus on sample-level condition prediction, there is some research jointly utilizing CNN and RNN on longitudinal audio data to model the respiratory abnormality progression.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…Compared to feature-based ML models, deep learning models do not depend on explicit feature engineering, so they usually present more powerful capability of modeling audio-disease relations with the premise of massive training data. The latest state-of-the-art audio-based respiratory condition screening methods are mainly deep learning based, covering convolutional neural networks (CNNs), 32 , 61 recurrent neural networks (RNNs), 59 , 60 and Transformer neural networks. 41 , 79 Those models have demonstrated favorable performance in detecting COPD, asthma, and other respiratory conditions.…”

Section: Introductionmentioning

confidence: 99%

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Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues

Tong

Mascolo

2022

Exp Biol Med (Maywood)

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Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues

Tong

Mascolo

2022

Exp Biol Med (Maywood)

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“…COVID-19 can be detected by cough sound ( 26 ). As reported by MIT ( 27 ), an artificial intelligence speech processing framework was developed, and COVID-19 was screened from cough sounds by using the processing feature extractor of cough sound signal.…”

Section: Related Workmentioning

confidence: 99%

A forced cough sound based pulmonary function assessment method by using machine learning

He²,

Chen³

et al. 2022

Front. Public Health

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Pulmonary function testing (PFT) has important clinical value for the early detection of lung diseases, assessment of the disease severity, causes identification of dyspnea, and monitoring of critical patients. However, traditional PFT can only be carried out in a hospital environment, and it is challenging to meet the needs for daily and frequent evaluation of chronic respiratory diseases. In this study, we propose a novel method for accurately assessing pulmonary function by analyzing recorded forced cough sounds by mobile device without time and location restrictions. In the experiment, 309 clips of cough sound segments were separated from 133 patients who underwent PFT by using Audacity software. There are 247 clips of training samples and 62 clips of testing samples. Totally 52 features were extracted from the dataset, and principal component analysis (PCA) was used for feature reduction. Combined with biological attributes, the normalized features were regressed by using machine learning models with pulmonary function parameters (i.e., FEV1, FVC, FEV1/FVC, FEV1%, and FVC%). And a 5-fold cross-validation was applied to evaluate the performance of the regression models. As described in the experimental result, the result of coefficient of determination (R2) indicates that the support vector regression (SVR) model performed best in assessing FVC (0.84), FEV1% (0.61), and FVC% (0.62) among these models. The gradient boosting regression (GBR) model performs best in evaluating FEV1 (0.86) and FEV1/FVC (0.54). The result confirmed that the proposed method was capable of accurately assessing pulmonary function with forced cough sound. Besides, the cough sound sampling by a smartphone made it possible to conduct sampling and assess pulmonary function frequently in the home environment.

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“…13.5. Discussion And ConclusionComparison between the proposed model was made with the two latest literature[36] and[17]. Both used the Virufy dataset but different classi cation methods.…”

mentioning

confidence: 90%

“…In [12], the RNN was implemented to differentiate between COVID-positive and healthy people. However, the RNN has long-term dependencies and is challenging to train for longer sequences of data due to the vanishing and exploding gradients [17]. LSTM is a particular type of RNN with memory cells, which addresses these challenges.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based COVID-19 Detection Method Using Coughing

Ankur

Kundu

Foysal

et al. 2022

Preprint

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COVID-19 has disrupted and irrevocably changed the everyday lives of people all around the world. This viral disease has created the necessity for a contact-free, non-invasive, and easy-to-use diagnostic device. In this paper, we propose a smartphone-based COVID-19 detection method that detects COVID-19 based on the coughing sound of patients. The proposed algorithm segments the coughing sounds collected from the raw audio signals acquired by a smartphone and then detects COVID-19 from the segmented coughing sounds. The proposed algorithm puts raw coughing sounds and the features extracted from the raw sounds into long-term short memory (LSTM), which is known to be effective in the regression and classification of periodic time series signals. Experimental results show that the proposed method applied to the Virufy dataset provides COVID-19 detection accuracy of 92% from the coughing segments. The proposed method has an advantage in pre-diagnosing COVID-19 since the proposed method only requires a smartphone Index Terms—COVID-19, LSTM., machine learning.

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CoughNet: A Flexible Low Power CNN-LSTM Processor for Cough Sound Detection

Cited by 13 publications

References 7 publications

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues

A forced cough sound based pulmonary function assessment method by using machine learning

LSTM-Based COVID-19 Detection Method Using Coughing

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