- Pfad:
-
Band XVIII. Armenwesen, Stiftungen und Wohlfahrtseinrichtungen
Zeitschrift
- Titel:
- Current directions in biomedical engineering
- Erschienen:
-
Berlin: De Gruyter
- Fußnote:
- Open Access
- Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
- Umfang:
- Online-Ressource
- ISSN:
- 2364-5504
- ZDB-ID:
-
2835398-5
- VÖBB-Katalog:
- 35423553
- Schlagworte:
- Zeitschrift
- ZLB-Systematik:
- Naturwissenschaften
- Dewey-Dezimalklassifikation:
- 570 Biowissenschaften, Biologie
- Sammlung:
- Naturwissenschaften
- Copyright:
- Rechte vorbehalten
- Zugriffsberechtigung:
- Freier Zugang
Aufsatz
- Titel:
- Predicting ECG Age in 24-hour Holter Recordings of Heart Failure Patients
- Erschienen:
-
Berlin: De Gruyter, 2025
- Sprache:
- Englisch
- Zusammenfassung:
- Abstract: Recent advances in deep learning enable estimation of a patient’s "ECG age" from standard short-term 12-lead electrocardiogram (ECG). This study introduces sequential ECG age predictions in continuous 24-hour Holter ECG recordings of chronic heart failure (CHF) patients. Using publicly available data from the MUSIC study, we analyzed data from 869 CHF patients, assessing both the differences (predicted ECG age vs actual chronological age) and dynamic -including variability and entropy-based complexity- to characterize temporal fluctuations over 24h. After automated removal of segments based on signal-to-noise ratio (SNR), 222 deceased patients were matched 1:1 with 222 surviving patients by sex, NYHA class, and age. While the predicted ECG age is similar across CHF patients, our findings indicate lower variability but increased complexity (approximate and sample entropy) in deceased compared to surviving patients, suggesting more irregular predicted ECG age dynamics among those who experienced adverse outcomes. Our results suggest that longitudinal evaluation of predictions from an end-to-end deep learning model can uncover subtle temporal dynamics potentially valuable for risk stratification.
- Umfang:
- Online-Ressource
- Fußnote:
- Open Access
- Archivierung/Langzeitarchivierung gewährleistet
- Schlagworte:
- electrocardiogram ; heart failure ; ECG age ; Holter monitoring ; entropy ; complexity ; deep learning ; biomarker
- ZLB-Systematik:
- Naturwissenschaften
- Medizin
- Sammlung:
- Naturwissenschaften
- Medizin
- Copyright:
- CC BY
- Zugriffsberechtigung:
- Freier Zugang
