Nigerian Schizophrenia EEG Dataset (NSzED) Towards Data-Driven Psychiatry in Africa (2311.18484v2)

Abstract: This work has been carried out to improve the dearth of high-quality EEG datasets used for schizophrenia diagnostic tools development and studies from populations of developing and underdeveloped regions of the world. To this aim, the presented dataset contains international 10/20 system EEG recordings from West African subjects of Nigerian origin in restful states, mental arithmetic task execution states and while passively reacting to auditory stimuli, the first of its kind from the region and continent. The subjects are divided into patients and healthy controls and recorded from 37 patients and 22 healthy control subjects identified by the Mini International Schizophrenia Interview (MINI) and also assessed by the Positive and Negative Symptoms Scale (PANSS) and the World Health Organization Disability Assessment Schedule (WHODAS). All patients are admitted schizophrenia patients of the Mental Health Ward, Medical Outpatient Department of the Obafemi Awolowo University Teaching Hospital Complex (OAUTHC, Ile-Ife) and its subsidiary Wesley Guild Hospital Unit (OAUTHC, Ilesa). Controls are drawn from students and clinicians who volunteered to participate in the study at the Mental Health Ward of OAUTHC and the Wesley Guild Hospital Unit. This dataset is the first version of the Nigerian schizophrenia dataset (NSzED) and can be used by the neuroscience and computational psychiatry research community studying the diagnosis and prognosis of schizophrenia using the electroencephalogram signal modality.

• The paper introduces NSzED, a comprehensive dataset addressing previous EEG study limitations in schizophrenia research.
• It employs the international 10/20 EEG system and rigorous diagnostics to capture biomarkers like MMN, SSR, and entropy.
• The dataset enables advanced feature fusion and computational analysis to refine early detection and objective diagnosis of schizophrenia.

Introduction to EEG and Schizophrenia

Electroencephalography (EEG) is a valuable non-invasive tool used to assess brain activity. It proves particularly useful in the exploration of neural functioning and potential functional anomalies in schizophrenia, a mental health disorder that significantly affects cognition, emotion, and behavior. While existing EEG datasets have contributed to advancements in early identification and prognosis prediction of schizophrenia, they also present limitations such as small sample sizes, biased representations, and a lack of standardization.

Addressing Dataset Limitations

Recognizing these limitations, a new dataset, called the Nigeria Schizophrenia EEG Dataset (NSzED), aims to enhance the resources available for the paper of schizophrenia, in particular for underrepresented populations from developing regions. This dataset includes EEG recordings of Nigerian individuals, both healthy controls and clinically diagnosed schizophrenia patients, across various conditions such as rest, mental arithmetic tasks, and reactions to auditory stimuli.

Dataset Composition and Methodology

NSzED's methodology involves the use of international 10/20 system EEG recordings, with participants selected based on rigorous diagnostic and functional assessment scales. The dataset consists of a well-defined mix of participants, presenting an opportunity to explore schizophrenia across diverse demographic spectra. Significantly, it offers a range of recordings that enable the calculation of several EEG biomarkers, including Mismatch Negativity (MMN), Steady State Response (SSR), and entropy-based measures.

Utility and Potential Impact

The dataset's organization allows for sophisticated feature fusion techniques to assess the efficacy of different EEG metrics. With its structured framework, NSzED supports in-depth computational analysis to advance our understanding of schizophrenia. This provision aims to set a new benchmark for EEG-based research and model development in schizophrenia diagnosis, making significant strides towards more accurate, objective, and inclusive diagnostic methods. The hope is that NSzED will not only refine current diagnostic models but also foster the discovery of novel markers for schizophrenia, especially for populations that have been historically underrepresented.