A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

Zheng, Xuejun; Zhang, Chun; Chen, Ping; Zhao, Kang; Jiang, Hanjun; Jiang, Zhiwei; Pan, Huafeng; Wang, Zhihua; Wen, Jia

doi:10.1109/access.2020.3020099

Cited by 13 publications

(13 citation statements)

References 30 publications

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“…The studies between 1967 and 2022 on this data set have been summarized in Table 1 [ 21 , 22 , 28 , 42 , 61 , 65 , 83 , 87 , 88 , 89 , 91 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ].…”

Section: Auscultation and Recording Technologiesmentioning

confidence: 99%

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

Redij

Kaur

Muddaloor

et al. 2023

Sensors

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Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device—the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson’s disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.

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Section: Auscultation and Recording Technologiesmentioning

confidence: 99%

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

Redij

Kaur

Muddaloor

et al. 2023

Sensors

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“…To capture the temporal information of the signals, we added two GRU layers of 32 units each before the final output sigmoid layer. This approach is based on previous literature on audio tagging [51] and ECG detection problems [52].…”

Section: Crnnmentioning

confidence: 99%

Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation

Ríos-Muñoz

Fernández‐Avilés

Arenal

2022

IJMS

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The maintaining and initiating mechanisms of atrial fibrillation (AF) remain controversial. Deep learning is emerging as a powerful tool to better understand AF and improve its treatment, which remains suboptimal. This paper aims to provide a solution to automatically identify rotational activity drivers in endocardial electrograms (EGMs) with convolutional recurrent neural networks (CRNNs). The CRNN model was compared with two other state-of-the-art methods (SimpleCNN and attention-based time-incremental convolutional neural network (ATI-CNN)) for different input signals (unipolar EGMs, bipolar EGMs, and unipolar local activation times), sampling frequencies, and signal lengths. The proposed CRNN obtained a detection score based on the Matthews correlation coefficient of 0.680, an ATI-CNN score of 0.401, and a SimpleCNN score of 0.118, with bipolar EGMs as input signals exhibiting better overall performance. In terms of signal length and sampling frequency, no significant differences were found. The proposed architecture opens the way for new ablation strategies and driver detection methods to better understand the AF problem and its treatment.

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“…Zheng et al [11] demonstrated another CRNN model for Gastrointestinal (GI) sound event detection. Their work employed a gastrointestinal sound dataset that includes 6 different types of body sounds, i.e.…”

Section: A Related Studies For Audio Classificationmentioning

confidence: 99%

“…This demonstrates again that the use of BiLSTM layer architectures can potentially increase classification performance. As the use of bidirectional RNN methods was shown to be advantageous in [11] and [14], the concept has been further explored in our research.…”

Section: B Bidirectional Rnn Architecturesmentioning

confidence: 99%

Deep Recurrent Neural Networks with Attention Mechanisms for Respiratory Anomaly Classification

Wall

Zhang

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses.

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A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

Cited by 13 publications

References 30 publications

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation

Deep Recurrent Neural Networks with Attention Mechanisms for Respiratory Anomaly Classification

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