Enhanced exploration of chronic cough using Improved Convolutional Neural Networks and remote monitoring harnessing Internet of Things (IoT)

Joshi, Aditi; Kumar, Rahul; Tiwari, Chandni

doi:10.1016/j.matpr.2021.03.641

Cited by 1 publication

(3 citation statements)

References 27 publications

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“…For use in embedded devices, Joshi et al [13] attached a sensor to the patient's upper body to detect cough sounds and provided information to clinicians and caregivers using Thing Speak IoT-cloud technology. Cough sounds are automatically detected for respiratory diseases using a reduced CNN model.…”

Section: Lightweight Modelsmentioning

confidence: 99%

“…All the features from the previous layer relate to the features of the current layer to deliver continuous information, thereby preventing the loss of feature information in the respiratory sounds. The formula for skip connections is that the x t is input to the BiGRU of (10), and the output (O t ) is calculated by ( 10)- (13). Fig.…”

Section: ) Dense Bigru Skip Connections Networkmentioning

confidence: 99%

“…Because it is part of the actual patient diagnosis, the accuracy is high, and the system must be time and memory-efficient for use in real-time applications [12]. Accordingly, the latest AI technology is being applied to computer network healthcare systems such as the internet of things (IoT) [13] cloud and edge computing technologies. Alotaibi and Subahi [14] proposed a method of deriving medical business goals using goal-oriented requirements extraction approach (GOREA), which includes modeling e-health business requirements and e-health IT and system requirements.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

Choi

Lee

et al. 2022

IEEE Access

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As the number of deaths from respiratory diseases due to COVID-19 and infectious diseases increases, early diagnosis is necessary. In general, the diagnosis of diseases is based on imaging devices (e.g., computed tomography and magnetic resonance imaging) as well as the patient's underlying disease information. However, these examinations are time-consuming, incur considerable costs, and in a situation like the ongoing pandemic, face-to-face examinations are difficult to conduct. Therefore, we propose a lung disease classification model based on deep learning using non-contact auscultation. In this study, two respiratory specialists collected normal respiratory sounds and five types of abnormal sounds associated with lung disease, including those associated with four lung lesions in the left and right anterior chest and left and right posterior chest. For preprocessing and feature extraction, the noise was removed using three pass filters (low, band, and high), and respiratory sound features were extracted using the Log-Mel Spectrogram-Mel Frequency Cepstral Coefficient followed by feature stacking. Then, we propose a lung disease classification model of dense lightweight convolutional neural network-bidirectional gated recurrent unit skip connections using depthwise separable convolution based on the extracted respiratory sound information. The performance of the classification model was compared with both the baseline and the lightweight models. The results indicate that the proposed model achieves high performance and has an accuracy of 92.3%, sensitivity of 92.1%, specificity of 98.5%, and f1-score of 91.9%. Using the proposed model, we aim to contribute to the early detection of diseases during the COVID-19 pandemic.

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Section: Lightweight Modelsmentioning

confidence: 99%

Section: ) Dense Bigru Skip Connections Networkmentioning

confidence: 99%