Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals

Göker, Hanife; Tosun, Mustafa

doi:10.1007/s00521-023-08971-6

Cited by 1 publication

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“…Researchers can look for migraine-related trends in a patient's clinical history by using time series evaluation. Applications for evaluating time series and SVM (support vector machines) and LSTM (long short-term memory networks) search for relationships between a person's various medical conditions and their headache patterns over time [6]. Doctors can diagnose and treat migraines more accurately by considering factors such as changes in lifestyle, exposure to environmental triggers, and fluctuations in hormone levels.…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

2024

JMC

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This study investigates how well time collection analysis may be used by system-studying algorithms to diagnose migraines. Through the use of various algorithms and current statistical resources, such as EEG activity and affected person histories, the mission will develop a predictive model to identify the start of migraine signs and symptoms, allowing for prompt and early management for sufferers. The results will help to compare how the algorithms affect migraine accuracy predictions and how well they forecast migraine presence early enough for preventative interventions. Furthermore, studies may be conducted to examine the model's ability to be employed in real-time patient monitoring and to identify actionable inputs from the algorithms. This work presents novel machine learning algorithms software for time series analysis of functions such as temperature, heart rate, and EEG indications, which can be used to identify migraines. The paper delves into the idea of utilizing machine learning algorithms to identify migraine styles, examines the pre-processing procedures to accurately arrange the indications, and provides the results of a study conducted to evaluate the efficacy of the solution. The observation's results show that the suggested diagnostic framework is capable of accurately identifying and categorizing migraines, enabling medical professionals to recognize the warning indications of migraine and predict when an attack would begin. The examination demonstrates the possibility of devices learning algorithms to correctly and accurately diagnose migraines, but more research is necessary to obtain more detailed information about this situation.

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Section: Introductionmentioning

confidence: 99%

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

2024

JMC

View full text Add to dashboard Cite

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Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals

Cited by 1 publication

References 50 publications

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

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