Emboli Detection by machine learning algorithms using multi-level feature extraction

Ab Waheed Lone*, Ahmet Elbir, Nizamettin Aydin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In real world, data understanding involves finding relationship between dependent and independents events, and usually this data is embedded in the form of images, audio, videos, speech, text and much more and to extract useful and representative information, feature extraction and representation is a front-end step. For machine learning algorithms, feature extraction is a preprocessing technique that helps in finding the relationship between different variables. In biomedical signal analysis such as cerebral emboli signal detection, feature extraction from samples constitutes an important step. In this paper, we performed two step feature extraction procedure. First we extracted Mel Frequency Cepstral Coefficients (MFCC) and biologically inspired Gamma-tone Cepstral Coefficients from Doppler signals. Second we extracted kurtosis and skewness parameters from all the Mel-frequency cepstral coefficients and Gamma-tone Cepstral Coefficients coefficients. To gain the understanding of extracted features from Doppler signals, we trained some machine learning algorithms such as k-nearest neighbors, support vector machines and logistic regression. GTCC based kurtosis and skewness features show better classification between emboli, artifact signals and Doppler speckle signals. We present evaluation results using confusion matrix for classification between emboli signals (ES), Doppler speckle (DS) and artifact signals (AS).

Original languageEnglish
Title of host publicationProceedings - 8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350309249
DOIs
Publication statusPublished - 2024
Event8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024 - Biskra, Algeria
Duration: 21 Apr 202422 Apr 2024

Publication series

NameProceedings - 8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

Conference

Conference8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024
Country/TerritoryAlgeria
CityBiskra
Period21/04/2422/04/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Cepstral Coefficients
  • Doppler signal
  • Emboli
  • Feature Extraction
  • Machine Learning

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