Advances in Biosignal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 740

Special Issue Editors


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Guest Editor
Institute of Computer Science of the Romanian Academy, Iasi Branch, 700481 Iasi, Romania
Interests: biosignal processing; biomedical image processing; artificial intelligence (neural networks, fuzzy systems, bio-inspired algorithms); (bio)sensors/transducers; e-health and telemedicine; assistive technologies
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Special Issue Information

Dear Colleagues,

This Special Issue is concerned with biosignal processing from ECG, EMG, EEG, and PPG signal information. Furthermore, it includes classification, prediction, diagnosis, biometrics, and various real-world applications based on deep learning and computational intelligence.

Dr. Keun-Chang Kwak
Prof. Dr. Hariton-Nicolae Costin
Guest Editors

Manuscript Submission Information

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Keywords

  • ECG signal processing
  • EMG signal processing
  • EEG signal processing
  • PPG signal processing
  • biosignal applications using deep learning
  • biosignal applications using computational intelligence

Published Papers (1 paper)

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Research

19 pages, 2189 KiB  
Article
Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification
by Monica Fira, Hariton-Nicolae Costin and Liviu Goras
Appl. Sci. 2024, 14(7), 3050; https://doi.org/10.3390/app14073050 - 4 Apr 2024
Viewed by 541
Abstract
We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people’s lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of [...] Read more.
We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people’s lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB® environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency. Full article
(This article belongs to the Special Issue Advances in Biosignal Processing)
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