PCA and SVM Technique for Epileptic Seizure Classification

Raibag, Mohammad Asif; Franklin, Janet

doi:10.1109/discover52564.2021.9663616

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In [54], a PCA+SVM model was proposed for diagnosing epileptic seizures by analyzing the features of electroencephalogram (EEG) recordings of brain activity. The proposed model was processed utilizing a dataset from UCI machine learning repository, which consists of 4097 EEG readings for 500 patients in 23.5 seconds.…”

Section: B Analysis On Existing Work On Pca+svm Techniques/modelsmentioning

confidence: 99%

Composing Efficient Computational Models for Real-Time Processing on Next-Generation Edge-Computing Platforms

Mohsin,

Shahrouzi,

Perera

2024

IEEE Access

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In the era of IoT and smart systems, an enormous amount of data will be generated from various IoT/smart devices in smart homes, smart cars, etc. Typically, this big data is collected and sent directly to the cloud infrastructure for processing, analyzing, and storing. However, traditional cloud infrastructure faces serious challenges when handling this massive amount of data, including insufficient bandwidth, high latency, unsatisfactory real-time response, high power consumption, and privacy protection issues. The edge-centric computing is emerging as a complementary solution to address the aforementioned issues of the cloud infrastructure. Furthermore, for many real-world IoT and smart systems, such as smart cars, real-time, in situ, and online data analysis and processing are crucial. With edge computing, data processing and analysis can be done closer to the source of the data (i.e., at the edge of the networks), which in turn enables real-time and in-situ data analytics and processing. As a result, edge computing will soon become the cornerstone of many IoT and smart applications. However, edge computing is still in its infancy; thus, requires novel models and techniques to support real-time and in-situ data processing and analysis. In this research work, we introduce novel and efficient computation models that are suitable for real-time processing and analysis on nextgeneration edge-computing platforms. Since most common edge-computing tasks are data analytics/mining, we focus on widely used data analytics techniques, including dimensionality reduction and classification techniques, specifically, principal component analysis (PCA) and support vectors machine (SVM), respectively. This is mainly because it is demonstrated that combination of PCA and SVM leads to high classification accuracy in many fields. In this paper, we introduce three different PCA+SVM models (i.e., Model 1, Model 2, and Model 3), for real-time processing and analysis (for online training and inference) on edge computing platforms. Model 1 and Model 2 are created utilizing the same SVM algorithm but with a different design/functional flows, whereas Model 3 is created with the same functional flow as Model 2 but utilizing a modified SVM algorithm. Our experimental results and analysis demonstrate that Model 3 utilizes dramatically lower number of iterations to produce the results, compared to that of other two models, while achieving acceptable performance results. Our results and analysis demonstrate that Model 3 is the most suitable computation model for real-time processing and analysis of edge computing platform.INDEX TERMS Edge computing, computational models, real-time processing, PCA+SVM, acceleration.

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Section: B Analysis On Existing Work On Pca+svm Techniques/modelsmentioning

confidence: 99%

Composing Efficient Computational Models for Real-Time Processing on Next-Generation Edge-Computing Platforms

Mohsin,

Shahrouzi,

Perera

2024

IEEE Access

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Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection

Wang,

Yuan,

Liu

et al. 2024

Int. J. Neur. Syst.

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Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

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HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals

Bhadra,

Singh,

Mahmud

2024

Brain Inf.

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Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.

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PCA and SVM Technique for Epileptic Seizure Classification

Cited by 3 publications

References 20 publications

Composing Efficient Computational Models for Real-Time Processing on Next-Generation Edge-Computing Platforms

Composing Efficient Computational Models for Real-Time Processing on Next-Generation Edge-Computing Platforms

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection

HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals

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