Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges

Stiehl, A.; Flammer, M.; Anselstetter, F.; Ille, N.; Bornfleth, H.; Geißelsöder, S.; Uhl, C.

doi:10.1109/icasspw59220.2023.10193167

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) 2023

DOI: 10.1109/icasspw59220.2023.10193167

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Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges

A. Stiehl,

M. Flammer,

F. Anselstetter

et al.

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Cited by 3 publications

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EnhancingEEGsignals classification usingLSTM‐CNNarchitecture

Omar,

Kimwele,

Olowolayemo

et al. 2023

Engineering Reports

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Epilepsy is a disorder that interferes with regular brain activity and can occasionally cause seizures, odd sensations, and momentary unconsciousness. Epilepsy is frequently diagnosed using electroencephalograph (EEG) records, although conventional analysis is subjective and prone to error. The dynamic and non‐stationary nature of EEG structure restricted the performance of Deep Learning (DL) approaches used in earlier work to improve EEG classification. Our multi‐channel EEG classification model, dubbed LConvNet in this paper, combines Convolutional Neural Networks (CNN) for extracting spatial features and Long Short‐Term Memory (LSTM) for identifying temporal dependencies. To discriminate between epileptic and healthy EEG signals, the model is trained using open‐source secondary EEG data from Temple University Hospital (TUH). Our model outperformed other EEG classification models employed in comparable tasks, such as EEGNet, DeepConvNet, and ShallowConvNet, which had accuracy rates of 86%, 96%, and 78%, respectively. Our model attained an amazing accuracy rate of 97%. During additional testing, our model also displayed excellent performance in trainability, scalability, and parameter efficiency.

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EnhancingEEGsignals classification usingLSTM‐CNNarchitecture

Omar,

Kimwele,

Olowolayemo

et al. 2023

Engineering Reports

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Persistent Homology-Based Classification of Chaotic Multi-variate Time Series: Application to Electroencephalograms

Flammer

2023

SN COMPUT. SCI.

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In this work, we present a combination of dimension reduction techniques and persistent homology for detection of epileptic events in electroencephalograms for a special kind of epilepsy called petit-mal epilepsy. Persistent homology, one of the main methods in topological data analysis, extracts information about the structures appearing in a given data set. Since during an epileptic seizure of the above type the electrical brain activity is more synchronized, we take the resulting structure in the EEG signal as classification feature that is analyzed topologically by means of persistent homology. As preprocessing step, the dimension of the data is reduced by two alternative techniques, principal component analysis and dynamical component analysis, and their performance is compared. Our results show that in comparison to principal component analysis, dynamical component analysis captures the dynamics of the system when projected onto a low-dimensional subspace. Furthermore, the results prove that persistent homology is well-suited for the detection of petit-mal epileptic seizures by means of their inherent structure.

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Advancing epilepsy diagnosis: A meta-analysis of artificial intelligence approaches for interictal epileptiform discharge detection

Borges Camargo Diniz,

Silva Santana,

Leite

et al. 2024

Seizure: European Journal of Epilepsy

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Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges

Cited by 3 publications

References 20 publications

EnhancingEEGsignals classification usingLSTM‐CNNarchitecture

EnhancingEEGsignals classification usingLSTM‐CNNarchitecture

Persistent Homology-Based Classification of Chaotic Multi-variate Time Series: Application to Electroencephalograms

Advancing epilepsy diagnosis: A meta-analysis of artificial intelligence approaches for interictal epileptiform discharge detection

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