Detection of schizophrenia: A machine learning algorithm for potential early detection and prevention based on event-related potentials

We show that event-related potentials can be used to detect schizophrenia with a high degree of precision. With our machine learning algorithm we achieve a balanced accuracy of 96.4 , which exceeds all results with comparable approaches. For this we use additional sensors on the left and right hemisphere in addition to the common central sensors. The experimental design when recording the data takes into account the dysfunction of the schizophrenic efference copy. Due to its serious consequences, schizophrenia is a social issue in which early detection and prevention plays a central role. In the future, machine learning could be used to support early interventions. When the first symptoms appear, potential patients could be tested for the dysfunction typical for schizophrenia. In this way, risk groups and potential patients could be adequately treated before the onset of psychosis

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method

We show that by unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum we can precisely detect alcoholism. Using this novel pre-processing step prior to entering a random forests classifier, our method substantially outperforms all previous results with a balanced accuracy of 97.4 percent. Our machine learning work contributes to healthcare and information systems. Due to its drastic and protracted consequences, alcohol consumption is always a critical issue in our society. Consequences of alcoholism in the brain can be recorded using electroencephalography (EEG). Our work can be used to automatically detect alcoholism in EEG mass data within milliseconds. In addition, our results challenge the medically outdated EEG standard bandwidths

A Novel Machine Learning Approach to Working Memory Evaluation Using Resting-State EEG Data

The human working memory and its cognitive functionality are essential for a range of fundamental to complex processes throughout our entire life. Damage and abnormalities can cause severe effects on an individual’s life. Predictive healthcare analytics can support and accelerate the diagnosis of such effects in the early stages. Using restingstate EEG data and the results of a cognitive testbattery for attentional performance, we developed a machine learning approach to evaluate the human working memory and detect hyperor hypoactivation. By predicting the testbattery results using the EEG recording of a patient, we enable a fast, objective, and accurate evaluation. Furthermore, we identified the most relevant brain regions (prefrontal cortex and dorsolateral prefrontal cortex) and the corresponding frequency subbands (9.511.5 Hz and 1113 Hz). With a balanced accuracy of 87.50%, our results set a new benchmark in evaluating the working memory using only restingstate EEG recordings

Multi-Class Emotion Recognition within the Valence-Arousal-Dominance Space Using EEG

Emotions play an important role in our daily life. Detecting emotions is a natural aspect of human communication and seems like a matter of course to us. However, the brain\u27s procedure of emotion processing is far more complex. We know little about the mechanisms of emotion regulation. Due to that, emotion recognition based on EEG brain signals has drawn much attention in research. This paper presents an approach for modeling and classifying different emotions within the Valence-Arousal-Dominance model. We investigate the effectiveness of high gamma frequencies (50-100 Hz) for emotion detection by dividing the standard EEG bandwidths into fine-graded point spectra, each with a span of 0.5 Hz. With an F1-score of 99.79\%, we not only show that our method is very well suited for discriminating different emotional states, but we also identify the most important high gamma frequency sub-bands. Our findings are consistent with other studies and further suggest the high gamma activity of the brain in emotion processing

High-Performance Detection of Mild Cognitive Impairment Using Resting-State EEG Signals Located in Broca’s Area: A Machine Learning Approach

Dementia and Alzheimer\u27s disease represent one of the biggest medical challenges of our century, manifesting the risk to individuals of losing their language or self-management skills. It is estimated that by 2050 around 1.2% of the world\u27s population will suffer from these diseases. Since there are no effective treatment options available, it is of great importance to detect dementia tendencies at the earliest possible stage. Mild cognitive impairment represents a preclinical stage of Alzheimer\u27s disease, allowing detecting dementia tendencies prematurely. As related work either relies on sensitive test results, does not manifest a sophisticated level of accuracy, or relies on costly techniques, further research is required. In this study, we propose a machine learning approach using resting-state EEG recordings of channels located in the Broca\u27s area. We achieved a benchmark accuracy of 90.91% in differentiating non-MCI and MCI individuals based on the assessment of phonemic verbal fluency

Machine Learning-Based Health Behavior Prediction Using Resting-State EEG Data

Long-term diseases often develop at a slow rate, making it difficult to detect them. These medical conditions often arise due to approached health behavior patterns. The circumstance of negative behavioral patterns encouraging diseases results in treatment issues due to late treatment initialization. This paper presents a machine learning-based approach to predict people’s health behavior tendencies at an early stage based on their Future Time Perspective using objective resting-state EEG data. With a balanced accuracy of 95.00 percent based on the EEG frequency bands identified as relevant (3.5 Hz-4.5 Hz, 4.5 Hz-5.5 Hz, 20 Hz-21 Hz, and 27 Hz-28 Hz), our model sets a first benchmark in this field, building a base for early recognition and intervention of potential long-term diseases