Search Results (2)

Panic disorder (PD) and focal epilepsy, in particular, temporal lobe epilepsy, often present diagnostic challenges due to overlapping clinical manifestations. This article describes the case of a 25-year-old female, misdiagnosed with PD for 15 years, whose recurring episodes of sudden fear, palpitations, and nausea were later identified as manifestations of focal epilepsy. Initially unresponsive to conventional anti-anxiety medications, the patient’s correct diagnosis was only established through comprehensive electro-clinical, neuropsychological, and neuroimaging evaluations during her admission to our research hospital. Long-term video–EEG monitoring (LTVEM) played a pivotal role in identifying the epileptic nature of her episodes, which were characterized by paroxysmal activity in the right temporal and zygomatic regions, consistent with the location of a dysplastic lesion in the right amygdala, as revealed by high-resolution magnetic resonance imaging. These findings underline the importance of considering focal epilepsy in the differential diagnosis of PD, especially in cases refractory to standard psychiatric treatments. The misdiagnosis of epilepsy as PD can lead to significant delays in appropriate treatment, potentially exacerbating the patient’s condition and affecting their quality of life. This case emphasizes the necessity of a multidisciplinary approach and the utilization of advanced diagnostic tools like LTVEM in elucidating the underlying causes of paroxysmal psychiatric symptoms. Full article

Epilepsy patients who do not have their seizures controlled with medication or surgery live in constant fear. The psychological burden of uncertainty surrounding the occurrence of random seizures is one of the most stressful and debilitating aspects of the disease. Despite the research progress in this field, there is a need for a non-invasive prediction system that helps disrupt the seizure epileptiform. Electroencephalogram (EEG) signals are non-stationary, nonlinear and vary with each patient and every recording. Full use of the non-invasive electrode channels is impractical for real-time use. We propose two frontal-temporal electrode channels based on ensemble empirical mode decomposition (EEMD) and Relief methods to address these challenges. The EEMD decomposes the segmented data frame in the ictal state into its intrinsic mode functions, and then we apply Relief to select the most relevant oscillatory components. A deep neural network (DNN) model learns these features to perform seizure prediction and early detection of patient-specific EEG recordings. The model yields an average sensitivity and specificity of 86.7% and 89.5%, respectively. The two-channel model shows the ability to capture patterns from brain locations for non-fontal-temporal seizures. Full article