A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition

Nemati, Nazanin; Meshgini, Saeed; Farzamnia, Ali

doi:10.1109/icee50131.2020.9260797

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In order to better diagnose lung cancer [29], Yanhao Tan et al proposed a method for automatically segmenting pulmonary nodules in CT images based on convolutional neural networks. N. Nemati et al proposed a lightweight classification model based on a deep convolutional neural network and using EEG signals obtained from the CHEG-MIT scalp EEG database for seizure prediction [30], with an accuracy of 99%. Through comparative experiments [31], Rekka Mastouri et al proved that in CT medical imaging analysis, the detection performance of the fine-tuned model based on the VGG16 model is higher than that of the model formed by transfer learning based on the pretrained VGG16 model.…”

Section: Related Workmentioning

confidence: 99%

“…Nida Khateeb [13] heart disease KNN 80% Allison M. Rossetto [15] lung cancer CNN 85.91% Hatim Guermah [20] chronic kidney disease SVM 93.3% Xuan Chen [21] pancreatic cancer ResNet18 91% Anik Saha [24] chronic kidney disease Neural Networks 97.34% Md. Golam Sarowar [25] tuberous sclerosis CNN 83.47% Ifthakhar Ahmed [26] myocardial infarction CNN 95.78% Muhammad Mubashir [28] lung cancer CNN 97.67% N. Nemati [30] epilepsy CNN 99% Xiang Yu [34] breast cancer Resnet-50 95.74%…”

Section: Researcher Disease Name Basic Algorithm Accuracymentioning

confidence: 99%

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Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction research, many datasets are collected by professional medical equipment. In addition, the currently researched models cannot be run directly on mobile terminals. In order to predict the health risks of the elderly without relying on professional medical testing equipment in the application scenarios of nursing homes, we use the datasets collected by non-professional medical testing equipment. Based on transfer learning and lightweight neural networks, we propose a disease risk prediction model, Diplin (disease risk prediction model based on lightweight neural network), applied to nursing homes. This model achieved 98% accuracy, 97% precision, 96% recall, 95% specificity, 97% F1 score, and 1.0 AUC (area under ROC curve) value on the validation set. The experimental results show that in the application scenario of nursing homes, the Diplin model can provide practical support for predicting the health risks of the elderly, and this model can be run directly on the tablet.

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Section: Related Workmentioning

confidence: 99%

Section: Researcher Disease Name Basic Algorithm Accuracymentioning

confidence: 99%

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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Advanced Deep Learning Frameworks for Classification of Epileptic Seizure Using Preprocessed EEG Signals

Biswash,

Khan,

Pall

2024

2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON)

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Seizure Detection Based on Lightweight Inverted Residual Attention Network

Lv,

Zhang,

Xiao

et al. 2024

Int. J. Neur. Syst.

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Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula: see text]M and the number of parameters is 0.57[Formula: see text]M.

show abstract

A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition

Cited by 3 publications

References 19 publications

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Advanced Deep Learning Frameworks for Classification of Epileptic Seizure Using Preprocessed EEG Signals

Seizure Detection Based on Lightweight Inverted Residual Attention Network

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