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    Öğe
    Feature Extraction for Biometric Recognition with Photoplethysmography Signals
    (Ieee, 2013) Kavsaoglu, A. Resit; Polat, Kemal; Bozkurt, M. Recep; Muthusamy, Hariharan
    Photoplethysmography (PPG) signals stand out due to features such as readily accessible, high reliability and confidentiality, the ease of use etc. among bio-signals. The feasibility studies carried out on the PPG signals demonstrated that PPG signals contained important features for human recognition and were the availability of biometric identification systems. In this study, twenty new features were extracted from PPG signal as a preliminary study intended to biometric recognition. PPG signals with 10 seconds were recorded from five healthy people using SDPPG (second derivative PPG) data acquisition card. To remove the noise from received raw PPG signals, a FIR low pass filtering with 200 points and 10 Hz cut-off frequency was designed. These twenty new features were obtained from filtered PPG signal and its second derivative. PPG signal with 10 seconds contains eight periods and twenty characteristic features in each person must not change within an individual over a period. This feature symbolizes the consistency in the identification of a person. To test the performance of biometric recognition system, the k-NN (k-nearest neighbor) classifier was used and achieved 95% of recognition success rate using 10-fold cross validation with twenty new features. The obtained results showed that the developed biometric recognition system based on PPG signal were very promising.
  • [ X ]
    Öğe
    Feature extraetion for biometrie reeognition with photoplethysmography signals
    (2013) Reşit Kavsaoglu, A.; Polat, Kemal; Recep Bozkure, M.; Muthusamy, Hariharan
    Photoplethysmography (PPG) signals stand out due to features such as readily accessible, high reliability and confidentiality, the ease of use etc. among bio-signals. The feasibility studies carried out on the PPG signals demonstrated that PPG signals contained important features for human recognition and were the availability of biometric identification systems. In this study, twenty new features were extracted from PPG signal as a preliminary study intended to biometric recognition. PPG signals with 10 seconds were recorded from five healthy people using SDPPG (second derivative PPG) data acquisition card. To remove the noise from received raw PPG signals, a FIR low pass filtering with 200 points and 10 Hz cut-off frequency was designed. These twenty new features were obtained from filtered PPG signal and its second derivative. PPG signal with 10 seconds contains eight periods and twenty characteristic features in each person must not change within an individual over a period. This feature symbolizes the consistency in the identification of a person. To test the performance of biometrie recognition system, the k-NN (k-nearest neighbor) classifier was used and achieved 95% of recognition success rate using lO-fold cross validation with twenty new features. The obtained results showed that the developed biometric recognition system based on PPG signal were very promising. © 2013 IEEE.
  • [ X ]
    Öğe
    Real Time Heart Rate Detection Using Non-Contact Photoplethysmography Signals
    (Ieee, 2014) Kavsaoglu, Ahmet Resit; Polat, Kemal; Bozkurt, Mehmet Recep
    Heart is contracted rhythmically so as to drive nutrients and oxygen necessary for life through our organs with blood arteries. The frequency for the rhythmic contraction of heart just as a pump is called heart rate (HR). Heart rate variation (HRV) is a measure of a fluctuation of time interval between heart beats. HRV is calculated considering electrodiagram (ECG) signals, arterial blood pressure signals or photoplethysmography (PPG) signals-derived time series of in-between heart beats. HRV is used as a significant indicator for the detection of healthiness and sickness state. Such pathological cases as high blood pressure, heart failure, and septic shock can be diagnosed using HRV. Therefore, accurate and rapid detection of HR is essential to correct diagnosis. In this study, real-time heart rate detection was derived from contactless PPG signals. PPG calling for contact with skin becomes useless in case of tissue scars or burns. In such cases, the use of contactless PPG is superior. Contactless PPG consists of a light source and a camera that senses reflection or transmittance of the light source. Camera images obtained were processed through an interface prepared in the MATLAB (TM) GUI setting, and real-time heart rate detection was carried out.