Italo Agustin Marsili1, Luca Biasiolli1, Michela Masè2, Alberto Adami1, Alberto Oliver Andrighetti1, Flavia Ravelli3, Giandomenico Nollo4. 1. Medicaltech Srl, Rovereto, Italy. 2. IRCS-HTA, Healthcare Research and Innovation Program, Fondazione Bruno Kessler, Trento, Italy; Department of Physics, University of Trento, Trento, Italy. Electronic address: michela.mase@unitn.it. 3. Department of Physics, University of Trento, Trento, Italy. 4. IRCS-HTA, Healthcare Research and Innovation Program, Fondazione Bruno Kessler, Trento, Italy; BIOtech Labs, Department of Industrial Engineering, University of Trento, Trento, Italy.
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.
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.