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

doi:10.1109/EMBC46164.2021.9631028

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 proceeding › Conference contribution

5 Scopus citations

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 language	English (US)
Title of host publication	2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publisher	IEEE
ISBN (Print)	978-1-7281-1180-3
DOIs	https://doi.org/10.1109/EMBC46164.2021.9631028
State	Published - 2021

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10.1109/EMBC46164.2021.9631028

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@inproceedings{e935db7cfa03455f88422e10bf5c58a8,

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

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{\textquoteright}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{\textquoteright}s statistical reliability and robustness.",

author = "Peihao Li and Evangelos Piliouras and Vahe Poghosyan and Majed AlHameed and Taous-Meriem Laleg-Kirati",

note = "KAUST Repository Item: Exported on 2021-12-16",

year = "2021",

doi = "10.1109/EMBC46164.2021.9631028",

language = "English (US)",

isbn = "978-1-7281-1180-3",

booktitle = "2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)",

publisher = "IEEE",

}

Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method. / Li, Peihao; Piliouras, Evangelos; Poghosyan, Vahe et al.
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method

AU - Li, Peihao

AU - Piliouras, Evangelos

AU - Poghosyan, Vahe

AU - AlHameed, Majed

AU - Laleg-Kirati, Taous-Meriem

N1 - KAUST Repository Item: Exported on 2021-12-16

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/674058

UR - https://ieeexplore.ieee.org/document/9631028/

U2 - 10.1109/EMBC46164.2021.9631028

DO - 10.1109/EMBC46164.2021.9631028

M3 - Conference contribution

C2 - 34891442

SN - 978-1-7281-1180-3

BT - 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

PB - IEEE

ER -

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

Abstract

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