Bowel sound recognition using SVM classification in a wearable health monitoring system

Yin, Yue; Jiang, Hanjun; Feng, Shulin; Liu, Juzheng; Chen, Ping; Zhu, Binjie; Wang, Zhihua

doi:10.1007/s11432-018-9395-5

Cited by 29 publications

(16 citation statements)

References 5 publications

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“…The same team then experimented with Legendre fitting, obtaining positive results [ 104 ]. In 2018, the group, based in Beijing and Shenzhen, presented a wearable BS monitoring system making use of a support vector machine [ 105 ]. In 2018, a team from Tsinghua University in Peking demonstrated that voice recognition neural networks can be applied to BS with high accuracy [ 106 ].…”

Section: Resultsmentioning

confidence: 99%

Automated Bowel Sound Analysis: An Overview

Nowak

Radzikowski

et al. 2021

Sensors

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Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.

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Section: Resultsmentioning

confidence: 99%

Automated Bowel Sound Analysis: An Overview

Nowak

Radzikowski

et al. 2021

Sensors

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“…Wireless sensor network (WSN) plays an important role in e-health via sensing, measuring, gathering patient's information for doctor's diagnosis, or recording in the medical server. Wearable health monitoring system (WHMS), one of the most popular application of e-health notation, has attracted extensive attention in academia and industry for its mobility, flexibility, and low cost [9][10][11][12]. WHMS is a WSN, with wearable sensors installed or implanted in the body of the patient, monitors the health conditions of patients by sensing, measuring, and gathering their physiological data and sends them to the medical professional or medical center via a wireless channel for proper diagnosis and further medical treatment.…”

Section: Introductionmentioning

confidence: 99%

An Improved Anonymous Authentication Protocol for Wearable Health Monitoring Systems

Shen

Wei-sheng

2020

Wireless Communications and Mobile Computing

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Wearable health monitoring system (WHMS), which helps medical professionals to collect patients’ healthcare data and provides diagnosis via mobile devices, has become increasingly popular thanks to the significant advances in the wireless sensor network. Because health data are privacy-related, they should be protected from illegal access when transmitted over a public wireless channel. Recently, Jiang et al. presented a two-factor authentication protocol on quadratic residues with fuzzy verifier for WHMS. However, we observe that their scheme is vulnerable to known session special temporary information (KSSTI) attack, privileged insider attack, and denial-of-service (DoS) attack. To defeat these weaknesses, we propose an improved two-factor authentication and key agreement scheme for WHMS. Through rigorous formal proofs under the random oracle model and comprehensive informal security analysis, we demonstrate that the improved scheme overcomes the disadvantages of Jiang et al.’s protocol and withstands possible known attacks. In addition, comparisons with several relevant protocols show that the proposed scheme achieves more security features and has suitable efficiency. Thus, our scheme is a reasonable authentication solution for WHMS.

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“…It has attracted a lot of attention due to its remarkable advantages: (1) effective in high dimensional spaces; (2) suitable for small samples set; (3) reasonable mathematic support; and (4) efficient perform as a non-linear classifier. In recent decades, SVM has been applied widely and successfully in solving classification problems, such as recognizing bowel sound [ 24 ], classifying vegetable pests [ 25 ], identifying alcohol consumption [ 26 ]. There are also some applications of SVM in the tobacco industry, such as classifying fragrant styles and evaluating the aromatic quality of flue-cured tobacco leaves [ 27 ], classifying the producing year of tobacco [ 12 ] and tobacco leaf grades [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

Wang

Xie

Yang

et al. 2018

Sensors

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Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.

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Bowel sound recognition using SVM classification in a wearable health monitoring system

Cited by 29 publications

References 5 publications

Automated Bowel Sound Analysis: An Overview

Automated Bowel Sound Analysis: An Overview

An Improved Anonymous Authentication Protocol for Wearable Health Monitoring Systems

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

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