Sparse Common Feature Analysis for Detection of Interictal Epileptiform Discharges from Concurrent Scalp EEG

TY - JOUR

T1 - Sparse Common Feature Analysis for Detection of Interictal Epileptiform Discharges from Concurrent Scalp EEG

AU - Abdi-Sargezeh, Bahman

AU - Valentin, Antonio

AU - Alarcon, Gonzalo

AU - Sanei, Saeid

N1 - Publisher Copyright: Author

PY - 2022

Y1 - 2022

N2 - Temporal interictal epileptiform discharges (IEDs) are often invisible in the scalp EEG (sEEG). However, due to within-electrode temporal correlation and between-electrode spatial correlation, they still have their signatures in the sEEG. Therefore, it is expected to have some common spatial and temporal features among the IEDs. In this paper, we first present a novel method, called common feature analysis (CFA)-based method, for IED detection via an existing common orthogonal basis extraction (COBE) algorithm. In the second approach, we benefit from the sparsity of IED waveforms in developing a new algorithm, namely sparse COBE, and based on that, a sparse CFA (SCFA)-based method for IED detection. The proposed CFA and SCFA models are compared with two state-of-the-art IED detection methods. Two types of approaches, namely within- and between-subject classification approaches, are employed for evaluating the methods. SCFA outperforms the others and achieves the accuracy values of 75.1% and 67.8% using within- and between-subject classification approaches, respectively. This enables the proposed techniques to capture the intracranial biomarkers of epilepsy and ameliorate the performance of a classifier in automatically detecting the scalp-invisible IEDs from sEEG.

AB - Temporal interictal epileptiform discharges (IEDs) are often invisible in the scalp EEG (sEEG). However, due to within-electrode temporal correlation and between-electrode spatial correlation, they still have their signatures in the sEEG. Therefore, it is expected to have some common spatial and temporal features among the IEDs. In this paper, we first present a novel method, called common feature analysis (CFA)-based method, for IED detection via an existing common orthogonal basis extraction (COBE) algorithm. In the second approach, we benefit from the sparsity of IED waveforms in developing a new algorithm, namely sparse COBE, and based on that, a sparse CFA (SCFA)-based method for IED detection. The proposed CFA and SCFA models are compared with two state-of-the-art IED detection methods. Two types of approaches, namely within- and between-subject classification approaches, are employed for evaluating the methods. SCFA outperforms the others and achieves the accuracy values of 75.1% and 67.8% using within- and between-subject classification approaches, respectively. This enables the proposed techniques to capture the intracranial biomarkers of epilepsy and ameliorate the performance of a classifier in automatically detecting the scalp-invisible IEDs from sEEG.

KW - Brain modeling

KW - Common feature analysis

KW - Electroencephalography

KW - Epilepsy

KW - Feature extraction

KW - IED detection

KW - interictal epileptiform discharges

KW - intracranial and scalp EEGs

KW - Recording

KW - Scalp

KW - sparsity

KW - Tensors

UR - http://www.scopus.com/inward/record.url?scp=85128265358&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2022.3167433

DO - 10.1109/ACCESS.2022.3167433

M3 - Article

AN - SCOPUS:85128265358

SN - 2169-3536

VL - 10

SP - 49892

EP - 49904

JO - IEEE Access

JF - IEEE Access

ER -