Assessment of the Topographic Memory in Epileptic Patients

Abstract Background Apathy in patients with epilepsy is associated with a wide range of consequences that reduce the patient’s ability to perform social functions and participate in self-care and rehabilitation programs. Therefore, apathy is one of the important diagnoses of the healthcare team in the process of caring for epileptic patients and its dimensions need to be examined and recognized. Therefore, appropriate instruments with the sociocultural milieu of each community should be provided to health care providers. The aim of the present study was to design and measure epilepsy–related apathy scale (E-RAS) in adults with epilepsy. Methods This study of sequential exploratory mixed methods design was conducted in Iran from April 2019 to December 2019. In the Item generation stage, two inductive (face-to-face and semi-structured interviews with 17 adult epileptic patients) and deductive (literature review) were used. In item reduction, integration of qualitative and literature reviews and scale evaluation were accomplished. For Scale Evaluation, face, content, construct [exploratory factor analysis (EFA) (n = 360) and confirmatory factor analysis (CFA) (n = 200)], convergent and divergent Validity and reliability (internal consistency and stability) were investigated. Results The results of EFA showed that E-RAS has four factors, namely, motivation; self-regulatory; cognition and emotional-effective. These four latent factors accounted for a total of 48.351% of the total variance in the E-RAS construct. The results of CFA showed that the 4-factor model of E-RAS has the highest fit with the data. The results of convergent and divergent validity showed that the values of composite reliability (CR) and average variance extracted (AVE) for the four factors were greater than 0.7 and 0.5, respectively, and the value of AVE for each factor was greater than CR. The Cronbach’s alpha coefficient for the whole scale was obtained 0.815. The results of the test-retest showed that there was a significant agreement between the test and retest scores (P < 0.001). Conclusion E-RAS is a multidimensional construct consisting of 24 items, and has acceptable validity and reliability for the study of epilepsy-related apathy in adult epileptic patients.

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Perception about Etiology of Epilepsy and Help-Seeking Behavior in Patients with Epilepsy

International Journal of Epilepsy ◽

10.1055/s-0041-1731933 ◽

2021 ◽

Author(s):

Gautam Das ◽

Samar Biswas ◽

Souvik Dubey ◽

Durjoy Lahiri ◽

Biman Kanti Ray ◽

...

Keyword(s):

Help Seeking ◽

Tertiary Care ◽

Epileptic Seizures ◽

Treatment Seeking ◽

Professional Help ◽

Epileptic Patients ◽

Seeking Behavior ◽

Tertiary Care Centers ◽

Patients With Epilepsy

Abstract Objectives Patients with epilepsy and their family have diverse beliefs about the cause of their illness that generally determine their treatment-seeking behavior. In this study, our aim was to find out different beliefs about epilepsy that lead to different help-seeking patterns, which act as barrier to the intended modern medical management of epilepsy. Materials and Methods One hundred and fifty consecutive consenting patients accompanied by a reliable informant/family member fulfilling the International Classification of Epileptic Seizures (ICES), simplified version, were included. Demographic and clinical data of all the eligible subjects was collected. Perceived cause of illness and help-seeking pattern were explored from patient/informant by administering proper instruments. Results Respondents identified varied causes of epilepsy and explored multiple help-seeking options before reaching tertiary care centers. We observed that, generally, epileptic patients/relatives who had belief in causes like supernatural causes sought help from nonprofessional personnel and those attributed their symptom to bodily pathology had professional help-seeking. Conclusions The belief in supernatural causes not being conformed to the biomedical models of the epileptic disorders increases the treatment gap.

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Epilepsy and quality of life in Iranian epileptic patients

Journal of Patient-Reported Outcomes ◽

10.1186/s41687-021-00292-3 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Bahareh Honari ◽

Seyed Mehran Homam ◽

Maryam Nabipour ◽

Zahra Mostafavian ◽

Arezou Farajpour ◽

...

Keyword(s):

Quality Of Life ◽

Negative Relationship ◽

Well Being ◽

Subscale Score ◽

Social Consequences ◽

Contributing Factors ◽

Epileptic Patients ◽

The Mean ◽

Spss Software

Abstract Background Epilepsy is one of the most common neurological disorders with physical, emotional, and social consequences. Previous studies indicate that epilepsy symptoms can highly affect the epileptic patients’ satisfaction in life. The aim of the present study is to investigate the QOL of People with Epilepsy (PWE) in Khorasan Razavi province, Iran. Methods In this study, 100 patients were randomly selected. After confirmation of the diagnosis of epilepsy by neurologists and fulfilling the entrance criteria, patients completed the Quality of Life in Epilepsy-31 inventory (QOLIE-31) questionnaire. Finally, data was analyzed statistically by SPSS software. Results The study sample comprised 100 PWE, aged 18–74 years (34 ± 13), of whom 58 (58%) were females. Tonic-colonic seizure was the most common (60%) type of seizure. The obtained score of each subscale and the range of the QOLIE-31 total score was 16.40–79.18 with the mean of 50 (SD = 16). The energy-fatigue subscale score was significantly higher in patients younger than 35 (p = 0.018). The data analysis showed that the seizure worry subscale was significantly higher in single patients (p = 0.04). Duration of epilepsy had a positive correlation with QOLIE-31 total score (p = 0.038), and a negative relationship with energy-fatigue subscale (p = 0.018). In contrast with previous studies, which reported the frequency of the epileptic episodes as the most important predictor of QOL, our results showed no significant correlation between the number of the episodes and overall QOL score (p = 0.063). However, the number of episodes was significantly correlated with emotional well-being and cognition subscales. Furthermore, the results indicated that poor QOL score is correlated with depressed mood. Conclusion In fact, the ultimate and preferred outcome of all treatments and care interventions is the patient’s QOL. Thus, improvement of the QOL by means of obtaining more information about its contributing factors, in PWE should be one of the main goals in the patients’ treatment.

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Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

Neuroinformatics ◽

10.1007/s12021-021-09538-3 ◽

2021 ◽

Author(s):

Parikshat Sirpal ◽

Rafat Damseh ◽

Ke Peng ◽

Dang Khoa Nguyen ◽

Frédéric Lesage

Keyword(s):

Near Infrared ◽

Short Term Memory ◽

A Priori ◽

Functional Near Infrared Spectroscopy ◽

Neural Data ◽

Frequency Bands ◽

Epileptic Patients ◽

Brain Hemodynamics ◽

Long Short Term Memory ◽

Trained Neural Network

AbstractIn this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model’s fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

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