Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device.
Comput Biol Med
; 116: 103540, 2020 01.
Article
em En
| MEDLINE
| ID: mdl-31751811
ABSTRACT
BACKGROUND:
Due to the growing epidemic of atrial fibrillation (AF), new strategies for AF screening, diagnosis, and monitoring are required. Wearable devices with on-board AF detection algorithms may improve early diagnosis and therapy outcomes. In this work, we implemented optimized algorithms for AF detection on a wearable ECG monitoring device and assessed their performance.METHODS:
The signal processing framework was composed of two main modules 1) a QRS detector based on a finite state machine, and 2) an AF detector based on the Shannon entropy of the symbolic word series obtained from the instantaneous heart rate. The AF detector was optimized off-line by tuning its parameters to reduce the computational burden while preserving detection accuracy. On-board performance was assessed in terms of detection accuracy, memory usage, and computation time.RESULTS:
The on-board implementation of the QRS detector produced an overall accuracy of 99% on the MIT-BIH Arrhythmia Database, with memory usageâ¯=â¯672 bytes, and computation time ≤90⯵s. The on-board implementation of the optimized AF algorithm gave an overall accuracy of 98.1% (versus 98.3% of the original version) on the MIT-BIH AF Database, with increased sensitivity (99.2% versus 98.5%) and decreased specificity (97.3% versus 98.2%), memory usageâ¯=â¯4648 bytes, and computation timeâ¯≤â¯75⯵sâ¯(consistent with real-time detection).CONCLUSIONS:
This study demonstrated the feasibility of real-time AF detection on a wearable ECG device. It constitutes a promising step towards the development of novel ECG monitoring systems to tackle the growing AF epidemic.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fibrilação Atrial
/
Algoritmos
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Diagnóstico por Computador
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Eletrocardiografia
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Dispositivos Eletrônicos Vestíveis
Tipo de estudo:
Diagnostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Revista:
Comput Biol Med
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Itália