Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone

Kutsumi, Yuka; Kanegawa, Norimasa; Zeida, Mitsuhiro; Matsubara, Hitoshi; Murayama, Norihito

doi:10.3390/s23010407

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…Yuka Kutsumi et al collected bowel sound (BS) data from 100 participants using a smartphone. They developed a CNN model which is capable of classifying BSs with an accuracy of 98% to comment on the gut health of a person [ 6 ]. Renisha Redij et al also illustrated the application of AI in classifying BSs.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

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Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.

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Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

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“…The process typically involves converting audio data into spectrograms, which serve as visual representations, which are then analyzed by supervised learning algorithms, particularly CNNs, to identify unique features. In the medical field, Kutsumi et al [32] utilized a CNN to analyze bowel sounds captured by smartphone microphones as a non-invasive measure of gastrointestinal health. Similarly, Peruzzi et al [33] explored the use of acoustics for diagnosing bruxism-related disorders, and Tariq et al [34] leveraged audio data from lung and heart sounds for the classification of various medical conditions, contributing to the advancement of early disease diagnostics across multiple health domains.…”

Section: Classification Based On Audio Datamentioning

confidence: 99%

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Mahbub,

Bhagwagar,

Chand

et al. 2024

Sensors

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Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

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“…Recent studies [ 21 , 22 ] have developed recording technologies to create a wearable device that is Bluetooth enabled for wireless transmission of data, thus allowing remote and telemedicine healthcare practices. A study by Kutsumi et al [ 111 ] recorded BS using a prototype application on a smartphone and successfully recognized BS using a CNN model. Hence, BS auscultation has a future potential to form a non-invasive diagnosis of various gastrointestinal disorders.…”

Section: Auscultation and Recording Technologiesmentioning

confidence: 99%

“…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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Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone

Cited by 15 publications

References 24 publications

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

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

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