title: Epileptic seizure onset detection based on EEG and ECG data fusion.
abstract: This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. Electronic address: marwa@tamu.edu.
authors.forename: Marwa
authors.initials: M
authors.lastname: Qaraqe
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. Electronic address: m.ismail@qatar.tamu.edu.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Ismail
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. Electronic address: serpedin@ece.tamu.edu.
authors.forename: Erchin
authors.initials: E
authors.lastname: Serpedin
authors.affiliation: Baylor Neurology Clinic, Baylor College of Medicine, 7200 Cambridge St. BCM 609, Houston, TX 77030, USA. Electronic address: Zulfi.Haneef@bcm.edu.
authors.forename: Haneef
authors.initials: H
authors.lastname: Zulfi
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D004562:Electrocardiography / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: ECG/EEG fusion; Heart rate variability; Seizure onset detection; Time–frequency analysis