A Novel Non-contact Infection Screening System Based on Self-Organizing Map with K-means Clustering

Sun, Guanghao; Abe, Shigeto; Takei, Osamu; Hakozaki, Yukiya; Matsui, Takemi

doi:10.1007/978-3-642-27157-1_14

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…For example, Wickramasinghe et al (2011) and Astel et al (2010) showed that SOM techniques were useful for identifying patterns in patients with diabetes, and these results increased the possibility of developing specific strategies to manage these patients efficiently. SOM analyses have also been used to evaluate the efficacy of a specific screening for assessing the presence of infection in people (e.g., Sun et al 2011), showing better results than linear discriminant analysis. Other studies have shown the utility of SOM in analyzing the satisfaction of patients in nursing science (Voutilainen et al 2014), identifying groups of children with different risk profiles for growth development (Schilithz et al 2014), or obtaining a deeper understanding of ventricular fibrillation (Rosado-Muñoz et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis

Oliver

Vallés-Ṕerez

Baños

et al. 2016

Sociological Methods & Research

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Registro de acceso restringido Este recurso no está disponible en acceso abierto por política de la editorial. No obstante, se puede acceder al texto completo desde la Universitat Jaume I o si el usuario cuenta con suscripción. Registre d'accés restringit Aquest recurs no està disponible en accés obert per política de l'editorial. No obstant això, es pot accedir al text complet des de la Universitat Jaume I o si l'usuari compta amb subscripció. Restricted access item This item isn't open access because of publisher's policy. The full--text version is only available from Jaume I University or if the user has a running suscription to the publisher's contents.

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Section: Discussionmentioning

confidence: 99%

Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis

Oliver

Vallés-Ṕerez

Baños

et al. 2016

Sociological Methods & Research

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“…In this paper, we demonstrate that LPNR-based signal separation can be applied to recordings obtained from medical radar. Medical radar has been proposed as contactless vital signs estimation method for use in infection screening systems (Sun et al, 2011a,b, 2012a,b, 2013; Yao et al, 2016) and depression screening systems (Sun et al, 2016). It works by recording the motion of the body surface induced by left ventricular ejection and aortic blood flow.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we demonstrate that this non-linear signal separation method can be adapted to mechanical heartbeat signals obtained with radar. Medical radar for non-contact vital signs acquisition is a rapidly developing modality, which faces very similar challenges to BCG, including sensitivity to movement artifacts, high variability in signal morphology and spectral overlap between cardiac and respiratory components (Sun et al, 2011a,b, 2012a, 2013, 2016; Khan and Cho, 2017).…”

Section: Introductionmentioning

confidence: 99%

Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation

et al. 2019

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Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis.

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“…To address these problems, a novel multimodal infection screening system has been developed, which is based on the contactless acquisition of heart rate, respiration rate and facial temperature [3]- [5]. The inclusion of additional vital-signs leads to increased screening accuracy [3], but also introduces the need for more sophisticated classification methods. In contrast to traditional screening systems based solely on thermography, classification using simple thresholding is not applicable anymore.…”

Section: Introductionmentioning

confidence: 99%

“…The multimodal screening system has been tested with several classification algorithms in different application scenarios, including linear discriminant analysis (LDA) [6] and an unsupervised algorithm based on self-organizing maps with k-means clustering [3]. Classification via support vector machine has been tested on an improved version of the screening system designed for use in paediatric wards [7] and logistic regressionbased classification has been tested in a study carried out in a hospital environment [8].…”

Section: Introductionmentioning

confidence: 99%

Multiple Vital-Sign-Based Infection Screening Outperforms Thermography Independent of the Classification Algorithm

Yao¹,

Sun

Matsui

et al. 2016

IEEE Trans. Biomed. Eng.

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A Novel Non-contact Infection Screening System Based on Self-Organizing Map with K-means Clustering

Cited by 4 publications

References 8 publications

Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis

Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis

Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation

Multiple Vital-Sign-Based Infection Screening Outperforms Thermography Independent of the Classification Algorithm

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