Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution.

Joseph Mathew, Natarajan Sivakumaran, P A Karthick

Abstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

Dynamic Global and Local Enhancement of Theta-Band Functional Networks as Neural Markers of Spinal Cord Stimulation Efficacy in Disorders of Consciousness.CNS neuroscience & therapeutics · 2026
Article

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5 · Who and what money

Authors and funding

3 authors.

Joseph MathewPhysiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India.

Natarajan SivakumaranPhysiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India.

P A KarthickPhysiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG. The features are computed from overlapping and non-overlapping moving windowing functions. The results show that the wavelet and spectral skewness of EEG is higher in EGSZ than in other types. All the extracted features are found to have significant differences (

Indexed as

encephalogramepilepsymachine learningmaximal overlap waveletseizures

Identifiers

PMID36832108

DOI10.3390/diagnostics13040621

PMCPMC9955002

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.