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

Frick, Janek; Rieg, Thilo; Buettner, Ricardo

doi:10.24251/hicss.2021.460

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Current research shows that the investigation of mental illnesses such as schizophrenia [52], [59], [60], or the investigation of certain personality traits based on EEG data [61], [62], show similar challenges, but can also be supported by the help of modern ML approaches.…”

Section: Introductionmentioning

confidence: 99%

A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

Penava,

Buettner

2023

IEEE Access

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We demonstrate how to use generative adversarial networks to improve the small data problem when training brain-computer-interfaces. The new approach is based on finely graded frequency bands, which are extracted from motor imagery electroencephalography data by using power spectral density method to synthetically generate electroencephalography data using generative adversarial networks. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets, by first checking the synthetic and real data for statistical and visual similarity, and secondly, by training a random forest classifier, once using only the real data and then using the real data augmented with the synthetic data. With similarity scores of 95.72 % in the subject-dependent case and 83.51 % in the subjectindependent case, and a predictive gain of 17.53 % in the subject-dependent case, and 7.51 % in the subjectindependent case, we were able to achieve promising results. The results show that our approach can make it possible to research rare diseases for which there is too little patient data. Also, synthetic data can be a way for many electroencephalography-based brain-computer interface applications to obtain the required data more cost-and time-efficiently.

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Section: Introductionmentioning

confidence: 99%

A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

Penava,

Buettner

2023

IEEE Access

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Schizophrenia Detection Using EEG: A Study on Frequency Relevance

Alexandre,

Duch

2024

2024 IEEE Congress on Evolutionary Computation (CEC)

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High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

Gross¹,

Breitenbach²,

Baumgartl³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Corneal Ulcer, also known as keratitis, represents the most frequently appearing symptom among corneal diseases, the second leading cause of ocular morbidity worldwide. Consequences such as irreversible eyesight damage or blindness require an innovative approach that enables a distinction to be made between patterns of different ulcer stages to lower the global burden of visual disability. This paper describes a Convolutional Neural Network-based image classification approach that allows the identification of different types of Corneal Ulcers based on fluorescein staining images. With a balanced accuracy of 92.73 percent, our results set a benchmark in distinguishing between general ulcer patterns. Our proposed method is robust against light reflections and allows automated extraction of meaningful features, manifesting a strong practical and theoretical relevance. By identifying Corneal Ulcers at an early stage, we aid reduction of aggravation by preventively applying and consequently tracking the efficacy of adapted medical treatment, which contributes to IT-based healthcare.

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

Cited by 4 publications

References 46 publications

A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

Schizophrenia Detection Using EEG: A Study on Frequency Relevance

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

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