Cardiac Signals: Remote Measurement and Applications

dc.contributor.authorSarkar, Abhijiten
dc.contributor.committeechairDoerzaph, Zachary R.en
dc.contributor.committeechairAbbott, A. Lynnen
dc.contributor.committeememberXuan, Jianhuaen
dc.contributor.committeememberStilwell, Daniel J.en
dc.contributor.committeememberParikh, Devien
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2017-08-26T08:00:18Zen
dc.date.available2017-08-26T08:00:18Zen
dc.date.issued2017-08-25en
dc.description.abstractThe dissertation investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals. We have mainly discussed two major cardiac signals: electrocardiogram (ECG), and photoplethysmogram (PPG). ECG and PPG signals hold strong potential for biometric authentications and identifications. We have shown that by mapping each cardiac beat from time domain to an angular domain using a limit cycle, intra-class variability can be significantly minimized. This is in contrary to conventional time domain analysis. Our experiments with both ECG and PPG signal shows that the proposed method eliminates the effect of instantaneous heart rate on the shape morphology and improves authentication accuracy. For noninvasive measurement of PPG beats, we have developed a systematic algorithm to extract pulse rate from face video in diverse situations using video magnification. We have extracted signals from skin patches and then used frequency domain correlation to filter out non-cardiac signals. We have developed a novel entropy based method to automatically select skin patches from face. We report beat-to-beat accuracy of remote PPG (rPPG) in comparison to conventional average heart rate. The beat-to-beat accuracy is required for applications related to heart rate variability (HRV) and affective computing. The algorithm has been tested on two datasets, one with static illumination condition and the other with unrestricted ambient illumination condition. Automatic skin detection is an intermediate step for rPPG. Existing methods always depend on color information to detect human skin. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. We have used LBP lacunarity based micro-textures features and a region growing algorithm to find skin pixels in an image. Our experiment shows that the proposed method is applicable universally to any image including near infra-red images. This finding helps to extend the domain of many application including rPPG. To the best of our knowledge, this is first such method that is independent of color cues.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:12419en
dc.identifier.urihttp://hdl.handle.net/10919/78739en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectElectrocardiogramen
dc.subjectBlood volume pulseen
dc.subjectRemote plethysmographyen
dc.subjectECG biometricsen
dc.subjectPPG biometricsen
dc.subjectSkin detectionen
dc.subjectDriver monitoringen
dc.subjectFace anti-spoofing.en
dc.titleCardiac Signals: Remote Measurement and Applicationsen
dc.typeDissertationen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en
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