Interpretable synthetic signals for explainable one-class time-series classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021113" target="_blank" >RIV/62690094:18470/24:50021113 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0952197623019000" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197623019000</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2023.107716" target="_blank" >10.1016/j.engappai.2023.107716</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpretable synthetic signals for explainable one-class time-series classification

Popis výsledku v původním jazyce

This research paper introduces an innovative approach for explainable one-class time-series classification (XOCTSC). The proposed method involves generating pseudounseen synthetic signals by altering the amplitude and cycle of the original signals. Subsequently, a classification process is performed to distinguish between the original and synthetic signals, and the resulting model is applied to testing data. Instances classified as synthetic classes are treated as unseen classes, and the dissimilarity with the training data can be elucidated through an explanation of the synthetic class creation process. This approach aims to enhance the interpretability of one-class time-series classification models by providing insights into the reasoning behind their decisions. The proposed method is demonstrated with a ballistocardiogram (BCG) signal for the breathing dataset and an electroencephalogram (EEG) signal for the epilepsy dataset. The proposed method recognizes BCG amplitude reduction during breath holding. Moreover, EEG cycle changes during epileptic seizures are observed across multiple channels. These observations align with actual epilepsy symptoms and breathing behaviors.

Název v anglickém jazyce

Interpretable synthetic signals for explainable one-class time-series classification

Popis výsledku anglicky

This research paper introduces an innovative approach for explainable one-class time-series classification (XOCTSC). The proposed method involves generating pseudounseen synthetic signals by altering the amplitude and cycle of the original signals. Subsequently, a classification process is performed to distinguish between the original and synthetic signals, and the resulting model is applied to testing data. Instances classified as synthetic classes are treated as unseen classes, and the dissimilarity with the training data can be elucidated through an explanation of the synthetic class creation process. This approach aims to enhance the interpretability of one-class time-series classification models by providing insights into the reasoning behind their decisions. The proposed method is demonstrated with a ballistocardiogram (BCG) signal for the breathing dataset and an electroencephalogram (EEG) signal for the epilepsy dataset. The proposed method recognizes BCG amplitude reduction during breath holding. Moreover, EEG cycle changes during epileptic seizures are observed across multiple channels. These observations align with actual epilepsy symptoms and breathing behaviors.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering applications of artificial intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

131

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

"Article number: 107716"

Kód UT WoS článku

001151379700001

EID výsledku v databázi Scopus

2-s2.0-85181951057