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dc.contributor.authorAydemir, Tuğba
dc.contributor.authorŞahin, Mehmet
dc.contributor.authorAydemir, Önder
dc.date.accessioned2022-09-26T07:04:54Z
dc.date.available2022-09-26T07:04:54Z
dc.date.issued2021en_US
dc.identifier.citationAydemir, T., Sahin, M. & (2021). Gender Recognition from PPG Signals of People with Different Blood Pressure. 29th Signal Processing and Communications Applications Conference (SIU 2021). http://doi.org/10.1109/SIU53274.2021.9477787en_US
dc.identifier.urihttp://doi.org/10.1109/SIU53274.2021.9477787
dc.identifier.urihttps://hdl.handle.net/11436/6544
dc.description.abstractPhotoplethysmography (PPG) is an opto-electronic technique that measures the blood volume within micro-vascular tissue. Studies based on measuring blood pressure level with PPG have been widely used. In this study, gender recognition was performed using PPG signals recorded from people with different blood pressure levels. The obtained results showed that the sum of the derivative, kurtosis, median, skewness, variance, variance of the second derivative calculated from the wavelet transform coefficients can be used in gender recognition as an attribute. By using these features, a classification accuracy of 74.77% and a polygon area metric of 0.4961 were obtained with the decision tree classifier. It is thought that gender recognition can be achieved with PPG signals, which can also be measured on touch screens (smart phones, watches and tablets) with the proposed method.en_US
dc.language.isoturen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBlood pressureen_US
dc.subjectGender recognitionen_US
dc.subjectPhotoplethysmographyen_US
dc.subjectClassificationen_US
dc.titleGender recognition from PPG signals of people with different blood pressureen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentRTEÜ, Fen - Edebiyat Fakültesi, Fizik Bölümüen_US
dc.contributor.institutionauthorAydemir, Tuğba
dc.contributor.institutionauthorŞahin, Mehmet
dc.identifier.doi10.1109/SIU53274.2021.9477787en_US
dc.relation.journal29th Signal Processing and Communications Applications Conference (SIU 2021)en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US


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