Spike pattern recognition by supervised classification in low dimensional embedding space

Evangelia I. Zacharaki (1, 2, 3) , Iosif Mporas (1) , Kyriakos Garganis (4) , Vasileios Megalooikonomou (1)

1 Department of Computer Engineering and Informatics [Patras]
2 CVN - Centre de vision numérique
3 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
4 St. Luke's Hospital [Thessaloniki]

Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using Support Vector Machines (SVM) in a low dimensional space on which the original waveforms are embedded by Locality Preserving Projections (LPP). The automatic detection results are compared to experts' manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97%) and the low false positive rate (0.1 min-1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.

Mots clés: spike detection; epilepsy; pattern recognition; manifold learning; dimensionality reduction

Domaines: Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Evangelia I. Zacharaki, Iosif Mporas, Kyriakos Garganis, Vasileios Megalooikonomou. Spike pattern recognition by supervised classification in low dimensional embedding space. Brain Informatics, 2016, 3, p. 73-83. ⟨10.1007/s40708-016-0044-4⟩. ⟨hal-01359155⟩