Multiscale Fluctuation Dispersion Entropy of EEG as a Physiological Biomarker of Schizotypy

Abstract

Altered electroencephalography (EEG) activity in schizotypal individuals is a powerful indicator of proneness towards psychosis. This alteration is beyond decreased alpha power often measured in resting state EEG. Multiscale fluctuation dispersion entropy (MFDE) measures the non-linear complexity of the fluctuations of EEGs and is a more effective approach compared to the traditional linear power spectral density (PSD) measures of EEG activity in patients with neurodegenerative disorders. In this study, we applied MFDE to EEG signals to distinguish high schizotypy (HS) and low schizotypy (LS) individuals. The study includes several trials from 29 participants psychometrically classified as HS (n=19) and LS (n=10). After preprocessing, MFDE was computed in frontal, parietal, central, temporal and occipital regions for each participant at multiple time scales. Statistical analysis and machine learning algorithms were used to calculate the differences in MFDE measures between the HS and LS groups. Our findings revealed significant differences in MFDE measures between LS and HS individuals in the delta frequency band (at time scale 100 ms). HS individuals exhibited increased complexity and irregularity compared to LS individuals in the delta frequency band particularly in the occipital region. Furthermore, the MFDE measures resulted in high accuracy (96.55%) in discriminating between HS and LS individuals and outperformed the models based on power spectrum, demonstrating the potential of MFDE as a neurophysiological marker for schizotypy traits. The increased non-linear fluctuation in delta frequency band in the occipital region of HS individuals implies the changes in cognitive functions, such as memory and attention, and has significant potential as a biomarker for schizotypy and proneness towards psychosis. INDEX TERMSSchizotypy, EEG, Nonlinear analysis, Complexity, Multiscale fluctuation dispersion entropy. I. INTRODUCTION S CHIZOTYPY is a mild stage of psychosis consisting of three dimensions, namely positive syndrome (paranormal beliefs, delusional beliefs, hearing voices, magical thinking and near-death experiences), negative syndrome (social and physical anhedonia) and disorganisation (illogical thinking and speech, poor concentration and memory) [1]-[4]. Disorganisation also consists of poor selective attention and anxiety in social interaction [5]-[7]. These traits are common in the general population [8], [9]. Schizotypy de-notes psychosis-like experiences but remains latent in the sub-clinical population [2]. Thus, studying schizotypy is important for understanding the risk factors for subsequent onset of schizophrenia (or other spectrum disorders). Electroencephalography (EEG) is a non-invasive and inexpensive neuroimaging technique and it provides reliable indicators for proneness towards psychosis [10], [11]. Several studies have identified altered resting-state EEG activity in schizotypal individuals [11]-[15]. Fuggetta et al. [11] observed higher alpha oscillation in the parieto-occipital brain VOLUM...

Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging

“…By extracting features, this study reduces dimensionality in the data and highlighted information that can be used for classification, such as time-frequency distributions [31]. A total of 74 features were extracted from each channel using different techniques, including: [35], [36]. One can gain a deeper understanding of the MFE approach's potential in sleep staging by acknowledging these advancements.…”

“…Further, it would be useful to compare the performance of MFE with Multiscale Dispersion Entropy (MDE) and Multiscale Fluctuation Dispersion Entropy (MFDE) methods in the context of sleep staging. In the recent literature, these methods have been demonstrated to be superior at detecting meaningful patterns[35],[36]. Future research based on MFE approaches can further enhance sleep staging algorithms by incorporating and evaluating these techniques.In this paper, an efficient and successful implementation of single-channel and multi-channel EEG-based neonatal sleep state classification using Multi-Branch CNN incorporating time and frequency domain features is proposed.…”

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

“…Electroencephalography (EEG) is highly nonlinear and entropy measures have long been used in clinical practice to reveal the nonlinear nature, for example, in classifying walking limitations [ 1 ], analyzing complexity and variability of trunk accelerations in patients with Parkinson’s Disease [ 2 , 3 ], differentiating balance patterns in diabetic patients with and without neuropathy [ 4 ], assessing anesthetic drug effects on the brain [ 5 ], identifying fetal distress [ 6 ], autism spectrum disorder in children [ 7 ], tinnitus [ 8 ], attention deficit hyperactivity disorder [ 9 ], epilepsy [ 10 ], Alzheimer’s disease [ 11 ], schizotypy [ 12 ], mind wandering [ 13 ] and psychogenic non-epileptic seizures [ 14 ]. Examples of entropy measures are permutation entropy [ 15 ], approximate entropy [ 16 ], neural network entropy [ 17 ], dispersion entropy [ 18 ], sample entropy [ 19 , 20 ] and their variants [ 21 – 25 ].…”