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Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

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Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

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Published on: March 10, 2017

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Emrah Aydemir¹, Turker Tuncer², Sengul Dogan²

¹Department of Computer Engineering, Engineering Faculty, Kirsehir Ahi Evran University, Kirsehir, Turkey.

Medical Hypotheses

|December 27, 2019

Summary

This summary is machine-generated.

This study introduces a novel multilevel machine learning approach for diagnosing epilepsy using electroencephalography (EEG) signals. The method achieved a high 98.4% success rate, demonstrating its potential for automated brain disease detection.

Keywords:

Electroencephalography signals classification K-nearest neighbors Machine learning Quadruple symmetric pattern Tunable-Q wavelet transform

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Last Updated: Jan 1, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

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Published on: March 10, 2017

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Published on: April 9, 2014

Area of Science:

Neuroscience
Biomedical Engineering
Computer Science

Background:

Electroencephalography (EEG) signals are crucial for diagnosing neurological disorders like epilepsy, Parkinson's Disease, and Multiple Sclerosis.
Automated diagnosis using machine learning (ML) on EEG data is an active area of research.

Purpose of the Study:

To propose and evaluate a multilevel machine learning method for automated epilepsy diagnosis using EEG signals.
To enhance the accuracy and efficiency of EEG-based epilepsy detection.

Main Methods:

A multilevel classification approach involving pre-processing, feature extraction, feature concatenation, feature selection, and classification.
Utilized Tunable-Q wavelet transform (TQWT) for signal decomposition and feature extraction.
Employed quadruple symmetric pattern (QSP) for feature extraction and Neighborhood Component Analysis (NCA) for feature selection, followed by k-nearest neighbors (kNN) classification.

Main Results:

The proposed method achieved a 98.4% success rate in classifying five classes using the Bonn EEG dataset.
Demonstrated effective feature extraction and selection for improved diagnostic accuracy.

Conclusions:

The developed multilevel machine learning method shows significant promise for accurate and automated epilepsy diagnosis from EEG data.
The approach is validated on a standard dataset and suggests potential for application in larger datasets for further validation.