Analysis of the Middle and Long Latency ERP Components in Schizophrenia

Costa, Miguel Rocha e; Teixeira, Felipe; Teixeira, João Paulo

doi:10.1007/978-3-030-91885-9_35

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…Abnormalities in functional and structural networks are associated with schizophrenia, in which they show less dispersion of excitation flow in prefrontal and premotor areas [ 73 ] with the prefrontal cortex (a region critical for working memory performance) being a brain region responsible for schizophrenia [ 83 , 86 ].…”

Section: Eeg In Schizophreniamentioning

confidence: 99%

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

et al. 2023

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Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC’s), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

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Section: Eeg In Schizophreniamentioning

confidence: 99%

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

et al. 2023

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Voice Patterns for Classification and Regression of Atherosclerotic Coronary Disease

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Morais

et al. 2023

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Background: Cardiovascular diseases are the main cause of mortality in both genders, being coronary artery disease the most prevalent type. Risk factors provide a limited help to estimate the presence of disease, acknowledging the need to investigate new techniques or biomarkers. In this study, it is our aim to evaluate the feasibility of using acoustic patterns of European Portuguese to infer about coronary disease; Methods: By collecting audio signals from patients diagnosed with heart disease and healthy subjects, a new database was developed, consisting of audio recordings and clinical metadata from a total of 84 participants. Using a combination of acoustic features, risk factors and clinical information, with distinct machine learning models, we explored binary classification and regression; Results: The Random Forests algorithm allowed to achieve a top accuracy of 88% for a binary classification (healthy vs disease) and ElasticNet allowed to achieve the minimum mean absolute error, 0.14, in a regression task. Fewer observations in higher CAD-RADS categories were limiting challenges; Conclusions: A combination of speech biomarkers and medical information can be used for identifying patterns of atherosclerotic coronary disease.

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Analysis of the Middle and Long Latency ERP Components in Schizophrenia

Cited by 2 publications

References 44 publications

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

Voice Patterns for Classification and Regression of Atherosclerotic Coronary Disease

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