A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Varone, Giuseppe; Boulila, ‪Wadii; Giudice, Michele Lo; Benjdira, Bilel; Mammone, Nadia; Ieracitano, Cosimo; Dashtipour, Kia; Neri, Sabrina; Gasparini, Sara; Morabito, Francesco Carlo; Hussain, Amir; Aguglia, Umberto

doi:10.3390/s22010129

Cited by 27 publications

(20 citation statements)

References 65 publications

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“…Leuchter et al demonstrated significant differences in functional brain connectivity patterns between subjects suffering from depression and healthy controls [ 58 ]. Varone et al found that functional connectivity analysis may be more effective than PSD in identifying psychogenic nonepileptic seizures within scalp EEG time series in the PNES study [ 59 ]. In summary, our work further supports the importance of functional connectivity in the parsing of EEG in depression from the perspective of multidimensional features.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Zhong

Wang

et al. 2023

Brain Sciences

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Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Zhong

Wang

et al. 2023

Brain Sciences

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“…SVM classifier: The SVM technique is a computer algorithm based on statistical theory to learn the labels assigned to objects. These support vectors try to find a hyperplane that separates the different classes of points in the hyperspace [ 25 ]. The SVM classifier was implemented in python using scikit-learn packages.…”

Section: Methodsmentioning

confidence: 99%

A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials

Niu

Shi

et al. 2022

Brain Sciences

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The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the process of error judgment. We designed an experiment in which we could activate the subject’s error-related potentials (ErrP). We recruited 26 subjects and asked them to wear EEG caps with cerebellar regions designed by us to participate in the experiment so that we could record their EEG activity throughout the experiment. We successfully mitigated the majority of noise interference after a series of pre-processing of the data collected from each subject. Our analysis of the preprocessed data revealed that our experiment successfully activated ErrP, and that the EEG signals, including the cerebellum, were significantly different when subjects made errors compared to when they made correct judgments. We designed a feature extraction method that requires selecting channels with large differences under different classifications, firstly by extracting the time-frequency features of these channels, and then screening these features with sequence backward feature (SBS) selection. We use the extracted features as the input and different event types in EEG data as the labels for multiple classifiers to classify the data in the executive and feedback segments, where the average accuracy for two-class classification of executive segments can reach 80.5%. The major contribution of our study is the discovery of the presence of ErrP in cerebellar regions and the extraction of an effective feature extraction method for EEG data.

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“…Since then, the advances in computing power and AI technologies have significantly increased the capabilities of clinical expert systems. Nowadays, expert systems (using neural network principles and ML techniques) can detect complex patterns and provide interpretations from large amounts of data, which is time- and effort-consuming in the case of manual processing [ 38 , 39 ]. For example, support vector machines [ 40 ] is used for the analysis, classification, and recognition of Parkinson’s [ 41 ] and Alzheimer’s diseases [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model

Mezzi

Yahyaoui

Krir

et al. 2022

Sensors

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For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day.

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A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Cited by 27 publications

References 65 publications

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials

Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model

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