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Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?

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The Fractal Geometry of the Brain

Part of the book series: Advances in Neurobiology ((NEUROBIOL,volume 36))

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Abstract

Depression is currently one of the most complicated public health problems with the rising number of patients, increasing partly due to pandemics, but also due to increased existential insecurities and complicated aetiology of disease. Besides the tsunami of mental health issues, there are limitations imposed by ambiguous clinical rules of assessment of the symptoms and obsolete and inefficient standard therapy approaches. Here we are summarizing the neuroimaging results pointing out the actual complexity of the disease and novel attempts to detect depression that are evidence-based, mostly related to electrophysiology. It is repeatedly shown that the complexity of resting-state EEG recorded in patients suffering from depression is increased compared to healthy controls. We are discussing here how that can be interpreted and what we can learn about future effective therapies. Also, there is evidence that novel options of treatment, like different modalities of electromagnetic stimulation, are successful just because they are capable of decreasing that aberrated complexity. And complexity measures extracted from electrophysiological signals of depression patients can serve as excellent features for further machine learning models in order to automatize detection. In addition, after initial detection and even selection of responders for further therapy route, it is possible to monitor the therapeutic flow for one person, which leads us to possible tailored treatment for patients suffering from depression.

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Authors and Affiliations

  1. Empa Swiss Federal Labs for Materials Science and Technology, St. Gallen, Switzerland

    Milena Čukić

  2. Nalez Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

    Elzbieta Olejarzcyk

  3. Tallinn University of Technology, Tallinn, Estonia

    Maie Bachmann

Authors
  1. Milena Čukić
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  2. Elzbieta Olejarzcyk
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  3. Maie Bachmann
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Correspondence to Milena Čukić .

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  1. Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia

    Antonio Di Ieva

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Čukić, M., Olejarzcyk, E., Bachmann, M. (2024). Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?. In: Di Ieva, A. (eds) The Fractal Geometry of the Brain. Advances in Neurobiology, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-031-47606-8_34

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