title: Interictal spike detection using the Walsh transform.
abstract: The objective of this study was to evaluate the feasibility of using the Walsh transformation to detect interictal spikes in electroencephalogram (EEG) data. Walsh operators were designed to formulate characteristics drawn from experimental observation, as provided by medical experts. The merits of the algorithm are: 1) in decorrelating the data to form an orthogonal basis and 2) simplicity of implementation. EEG recordings were obtained at a sampling frequency of 500 Hz using standard 10-20 electrode placements. Independent sets of EEG data recorded on 18 patients with focal epilepsy were used to train and test the algorithm. Twenty to thirty minutes of recordings were obtained with each subject awake, supine, and at rest. Spikes were annotated independently by two EEG experts. On evaluation, the algorithm identified 110 out of 139 spikes identified by either expert (True Positives = 79%) and missed 29 spikes (False Negatives = 21%). Evaluation of the algorithm revealed a Precision (Positive Predictive Value) of 85% and a Sensitivity of 79%. The encouraging preliminary results support its further development for prolonged EEG recordings in ambulatory subjects. With these results, the false detection (FD) rate is estimated at 7.2 FD per hour of continuous EEG recording.
authors.affiliation: Department of Electrical & Computer Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL 33174, USA. adjouadi@eng.fiu.edu
authors.forename: Malek
authors.initials: M
authors.lastname: Adjouadi
authors.forename: Danmary
authors.initials: D
authors.lastname: Sanchez
authors.forename: Mercedes
authors.initials: M
authors.lastname: Cabrerizo
authors.forename: Melvin
authors.initials: M
authors.lastname: Ayala
authors.forename: Prasanna
authors.initials: P
authors.lastname: Jayakar
authors.forename: Ilker
authors.initials: I
authors.lastname: Yaylali
authors.forename: Armando
authors.initials: A
authors.lastname: Barreto
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted