Physiological Signals Fusion Oriented to Diagnosis - A Review

Uribe, Y. F.; Álvarez-Uribe, Karla C.; Peluffo-Ordóñez, Diego H.; Becerra, Miguel A.

doi:10.1007/978-3-319-98998-3_1

Cited by 3 publications

(7 citation statements)

References 78 publications

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“…Physiological signals are an invaluable data source which deliver relevant information on the status of the human being [24]. Physiological signals reflect the electrical activity or status of a specific body part [25].…”

Section: Physiological Signalsmentioning

confidence: 99%

“…Therefore, sensors location determines signal characteristics [26]. In addition, there are a large number of biomedical devices which extract and measure physiological signals, such as: multiparameter vital signs monitors, electroencephalograms, electrocardiograms, electromyograms, thermometers, motion sensors, oxygen saturation sensors, glucometers, among others [24].…”

Section: Physiological Signalsmentioning

confidence: 99%

“…Consequently, classifying physiological signals seems natural in order to understand their source and what are they used sampling. Uribe et al [24] In this section, different physiological signals measurement methods are reviewed including current available datasets.…”

Section: Physiological Signalsmentioning

confidence: 99%

“…As it was mentioned before, physiological signals are data which delivers relevant information about human body. Also, the above classifcation presented by Uribe et al [24] was made taking into account signals source. In this sense, knowing how physiological signals are measured provides relevant information about their charecteristics as signals.…”

Section: Measurementmentioning

confidence: 99%

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Literature Review of Deep Learning for Physiological signal Analysis

Ortiz,

Beleño,

Pérez

et al. 2023

Preprint

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Deep Learning (DL) has proved to be a promising methodology for classification, recognition, prediction and end-to-end tasks. Recently it has proved its high potential in healthcare applications demostrating to be a succesful approach for physiological signals analysis. Therefore, in this paper we survey the lastest scientific research on deep learning in physiological signal data. The purpose of this paper is to indentify the lastest contributions on this field and overview lastest deep learning approaches.

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Section: Physiological Signalsmentioning

confidence: 99%

Section: Physiological Signalsmentioning

confidence: 99%

Section: Physiological Signalsmentioning

confidence: 99%

Section: Measurementmentioning

confidence: 99%

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Literature Review of Deep Learning for Physiological signal Analysis

Ortiz,

Beleño,

Pérez

et al. 2023

Preprint

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“…In this regard, data fusion has gained much attention and been used in many applications in different fields such as sensor networks [8], fault diagnosis in maintenance [9], internet of things [10], eye movement recognition [11], economic data analysis [12], environmental hazard events [13], acoustic sensor networks [14], target tracking [15], robotics [16], image processing [17], intelligent systems designing, health applications [18], biometrics [19], surveillance [20], and human capital [21] approaches. In this study, the terms "information fusion" and "data fusion" are employed indistinctly for simplicity purposes.…”

Section: Introductionmentioning

confidence: 99%

Information Quality Assessment for Data Fusion Systems

Becerra

Tobón

Castro-Ospina

et al. 2021

Data

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This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered.

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