Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Pisano, F.; Sias, G.; Fanni, Alessandra; Cannas, B.; Dourado, António; Pisano, Maria Barbara; Teixeira, César

doi:10.1155/2020/4825767

Cited by 28 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantage and disadvantages of the accelerometers method to detect the seizures is discussed in detail in (Nijsen et al, 2005; Nijsen et al, 2010) and authors revealed that this method requires less motor activity with better comfort to wear the device. In (Pisano et al, 2020), authors applied patient specific seizure detection using CNN which limits the time consumption for the doctors in classifying the EEG signal has seizure or not. Features learned by the convolution layers are applied directly to the transfer learning technique for fine tuning the CNN model.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

Sabarivani

Ramadevi

2022

Concurrent Engineering

View full text Add to dashboard Cite

In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

Sabarivani

Ramadevi

2022

Concurrent Engineering

View full text Add to dashboard Cite

show abstract

“…e model's first layer is composed of real EEG signals. e CNN model that follows after the input layer requires fourstep periods and eight filtering with 23 elements to conduct a combination on the original signal [26]. Extracted features of the carrier frequency are formed after the convolution process.…”

Section: E Rectified Linear Activation Unitmentioning

confidence: 99%

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN

Syamsundararao¹,

Selvarani

Rathi

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain’s function. EEG is an effective instrument for deciphering the brain’s complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader’s level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one-dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre-ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.

show abstract

“…CNNs have achieved the remarkable results in the seizure detection, [44][45][46][47] the seizure control 48 and the detection of interictal epileptiform discharges. 49 A CNN model generally consists of convolution layers, pooling layers and fully connected layers.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG

Wang

Zhang

Wang

et al. 2021

Int. J. Neur. Syst.

View full text Add to dashboard Cite

Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

show abstract

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Cited by 28 publications

References 34 publications

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN

One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG

Contact Info

Product

Resources

About