Epilepsy is a neurological disease that affects the brain and is characterised by repeated seizures. Generalised, focal and unknown are three major types of seizures. Each type has several subgroups. For this reason, seizure detection and classification are expensive and erroneous. Other factors can also affect the detection. For example, patients can have a combination of different seizures or...

Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It thus crucial develop automated models detection abnormalities in EEGs related epilepsy. This paper describes development a novel class compact convolutional neural networks (CNNs) detecting abnormal patterns electrodes The designed model inspired by CNN...

PURPOSE To assess if 3T MRI can be further improved by adding surface coil imaging, in the context of detection and characterization of cerebral lesions in patients with drug-resistant epilepsy. METHODS Twenty five patients with drug-resistant epilepsy undergoing evaluation for epilepsy surgery were examined with high resolution 3T MRI. The patients were MRI-negative (n = 15), or had unclear ...

Signal processing has varied range of applications from our daily life signal processing is involved. From the communication, artificial intelligence, advance robotics to the advance bio medical applications like ECG, EEG processing etc. In this paper we have studied. EEG signal as a part of signal processing for diagnostic understanding of epilepsy. Epilepsy is one of the most common neurologi...

OBJECTIVE Epilepsy and intellectual/developmental disabilities (ID/DD) have a high rate of co-occurrence. Here, we investigated gene mutations in Chinese children with unexplained epilepsy and ID/DD. METHODS We used targeted next-generation sequencing to detect mutations within 300 genes related to epilepsy and ID/DD in 253 Chinese children with unexplained epilepsy and ID/DD. A series of fil...

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of electrocardiographic changes in patients with partial epilepsy. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Two...

Epilepsy is one of the most common neurological disorders that greatly impair patients’ daily lives. A classifier for automated epileptic EEG detection and patient monitoring can be very important for epilepsy diagnosis and patients’ quality of life, especially for rural areas and developing countries where medical resources are limited. This paper describes our development of an accurate and f...

Epilepsy is common neurological disorder disease in the world. Electroencephalogram (EEG) can provide significant information about epileptic activity in human brain. Since detection of the epileptic activity requires analyzing of very length EEG recordings by an expert, researchers tend to improve automated diagnostic systems for epilepsy in recent years. In this work, we try to automate detec...

The present paper attempts to introduce a new approach for the automatic detection of epileptic spikes in EEG signal which plays a vital role in diagnosing epilepsy. In this approach, we have detected and classified epileptic spikes by using extracted features and Fuzzy ARTMAP neural network. The performance of classifying system is evaluated by three criteria of sensitivity, selectivity and sp...