TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals.
Background and Purpose: Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a no...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
3/24/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185071011&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185071011 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 3/24/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185071011 185071011 185071011 10.1007/s10916-025-02169-0 185071011 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals. aug: au: Gelen, Mehmet Ali Tuncer, Turker Baygin, Mehmet Dogan, Sengul Barua, Prabal Datta Tan, Ru-San Acharya, U. R. affil: Department of Cardiology, Elazig Fethi Sekin City Hospital, Elazig, Turkey sug: subj: Plethysmography Methods Arrhythmia Diagnosis Engineering Signal Processing, Computer Assisted Human Arrhythmia Classification Electrodiagnosis Chi Square Test Automation Premature Ventricular Contractions Diagnosis Premature Atrial Contractions Diagnosis Tachycardia, Ventricular Diagnosis Tachycardia, Supraventricular Diagnosis Atrial Fibrillation Diagnosis Descriptive Statistics Support Vector Machine Convolutional Neural Networks ab: Background and Purpose: Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals. Method: We have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion. Results: Our proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation. Conclusion: Our results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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