Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method

Peihao Li, Evangelos Piliouras, Vahe Poghosyan, Majed AlHameed, Taous-Meriem Laleg-Kirati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we utilize a signal processing tool, which can help physicians and clinical researchers to automate the process of EEG epileptiform spike detection. The semi-classical signal analysis method (SCSA) is a data-driven signal decomposition method developed for pulse-shaped signal characterization. We present an algorithm framework to process and extract features from the patient’s EEG recording by deriving the mathematical motivation behind SCSA and quantifying existing spike diagnosis criterion with it. The proposed method can help reduce the amount of data to manually analyse. We have tested our proposed algorithm framework with real data, which guarantees the method’s statistical reliability and robustness.
Original languageEnglish (US)
Title of host publication2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
PublisherIEEE
ISBN (Print)978-1-7281-1180-3
DOIs
StatePublished - 2021

Fingerprint

Dive into the research topics of 'Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method'. Together they form a unique fingerprint.

Cite this