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  • Title: Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device.
    Author: Marsili IA, Biasiolli L, Masè M, Adami A, Andrighetti AO, Ravelli F, Nollo G.
    Journal: Comput Biol Med; 2020 Jan; 116():103540. PubMed ID: 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 usage = 672 bytes, and computation time ≤90 μs. 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 usage = 4648 bytes, and computation time ≤ 75 μs (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.
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