Classifying Dysphagic Swallowing Sounds with Support Vector Machines

Miyagi, Shigeyuki; Sugiyama, Syo; Kozawa, Keiko; Moritani, Sueyoshi; Sakamoto, Shinichi; Sakai, Osamu

doi:10.3390/healthcare8020103

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…Even if the training dataset are not large enough, SVM classifier can achieve a good classification performance 23 . Up to now, the SVM has become one of most widely used classifiers, which has been applied in various classification research fields 24 , 25 .…”

Section: Discussionmentioning

confidence: 99%

An effective detection method for wheat mold based on ultra weak luminescence

Gong

Liang

et al. 2022

Sci Rep

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It is widely known that mold is one of important indices in assessing the quality of stored wheat. First, mold will decrease the quality of wheat kernels; the wheat kernels infected by mold can produce secondary metabolites, such as aflatoxins, ochratoxin A, zearalenone, fumonisins and so on. Second, the mycotoxins metabolized by mycetes are extremely harmful to humans; once the food or feed is made of by those wheat kernels infected by mold, it will cause serious health problems on human beings as well as animals. Therefore, the effective and accurate detection of wheat mold is vitally important to evaluate the storage and subsequent processing quality of wheat kernels. However, traditional methods for detecting wheat mold mainly rely on biochemical methods, which always involve complex and long pretreatment processes, and waste part of wheat samples for each detection. In view of this, this paper proposes a type of eco-friendly and nondestructive wheat mold detection method based on ultra weak luminescence. The specific implementation process is as follows: firstly, ultra weak luminescence signals of the healthy and the moldy wheat subsamples are measured by a photon analyzer; secondly, the approximate entropy and multiscale approximate entropy are introduced as the main classification features separately; finally, the detection model has been established based on the support vector machine in order to classify two types of wheat subsamples. The receiver operating characteristic curve of the newly established detection model shows that the highest classification accuracy rate can reach 93.1%, which illustrates that our proposed detection model is feasible and promising for detecting wheat mold.

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Section: Discussionmentioning

confidence: 99%

An effective detection method for wheat mold based on ultra weak luminescence

Gong

Liang

et al. 2022

Sci Rep

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“…Esta abordagem tem incentivado o desenvolvimento de dispositivos de custo cada vez mais acessível para analisar e monitorar a deglutição em tempo real e em situações cotidianas, sobretudo durante as refeições. Estudos apontam que as tecnologias vestíveis permitem gerar algoritmos com propriedades ótimas de medida para classificar indivíduos quanto às condições de deglutição (20,(23)(24)(25)(26) , sendo possível encontrar registros promissores do uso de métodos de aprendizagem de máquina como Redes Neurais Profundas (20,23,24,27) , Máquinas de Vetores Suporte (SVMs -Support Vector Machines) (28) e Análise Discriminante Linear (LDA -Linear Discriminant Analysis) (29) . A utilização de grande volume de dados (big data) para treinamento desses sistemas possibilitará definir modelos de aprendizagem profunda cada vez mais robustos e confiáveis para realizar análise automática de parâmetros de deglutição.…”

Section: Prezadas Editorasunclassified

O uso de tecnologias vestíveis para análise e monitoramento de funções relacionadas à alimentação e comunicação

Costa

Dantas

Machado

et al. 2022

CoDAS

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Este é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições desde que o trabalho original seja corretamente citado. O uso de tecnologias vestíveis para análise e monitoramento de funções relacionadas à alimentação e comunicaçãoWearable technology use for the analysis and monitoring of functions related to feeding and communication

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“…The application of ML in acoustics is a field of research that has recently attracted great interest in the scientific community. Application examples can be found in a wide range of acoustics fields, such as speech signal processing [17], underwater acoustics [18], medical diagnosis [19], design of acoustic materials [20], bioacoustics [21], room acoustics [22] and environmental acoustics [23].…”

Section: Machine Learning For Analysis Of Environmental Acousticsmentioning

confidence: 99%

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

Pita

Rodríguez

Navarro

2021

IJERPH

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As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.

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Classifying Dysphagic Swallowing Sounds with Support Vector Machines

Cited by 14 publications

References 25 publications

An effective detection method for wheat mold based on ultra weak luminescence

An effective detection method for wheat mold based on ultra weak luminescence

O uso de tecnologias vestíveis para análise e monitoramento de funções relacionadas à alimentação e comunicação

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

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