title: Seizure detection method based on fractal dimension and gradient boosting.
abstract: Automatic seizure detection technology is necessary and crucial for the long-term electroencephalography (EEG) monitoring of patients with epilepsy. This article presents a patient-specific method for the detection of epileptic seizures. The fractal dimensions of preprocessed multichannel EEG were firstly estimated using a k-nearest neighbor algorithm. Then, the feature vector constructed for each epoch was fed into a trained gradient boosting classifier. After a series of postprocessing, including smoothing, threshold processing, collar operation, and union of seizure detections in a short time interval, a binary decision was made to determine whether the epoch belonged to seizure status or not. Both the epoch-based and event-based assessments were used for the performance evaluation of this method on the EEG data of 21 patients from the Freiburg dataset. An average epoch-based sensitivity of 91.01% and a specificity of 95.77% were achieved. For the event-based assessment, this method obtained an average sensitivity of 94.05%, with a false detection rate of 0.27/h.
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China; School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, China.
authors.forename: Yanli
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute of Shandong University, Suzhou 215123, China. Electronic address: wdzhou@sdu.edu.cn.
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute of Shandong University, Suzhou 215123, China.
authors.forename: Shasha
authors.initials: S
authors.lastname: Yuan
authors.affiliation: Suzhou Institute of Shandong University, Suzhou 215123, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000295:instrumentation / Q000706:statistics & numerical data*; D005189:False Positive Reactions; D017709:Fractals; D006801:Humans; D018802:Patient-Centered Care; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*
keywords: EEG; Fractal dimension; Gradient boosting; Seizure detection