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AI
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researcher student
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60
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Title | Updated date | Comment count |
---|---|---|
Arrhythmia Analysis Accelerator : A-Cube | 4 weeks ago | 3 |
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We propose the A-Cube design methodology to create medical decision support on the edge. The design and implementation of an atrial fibrillation detector hardware core was selected as a proof-of-concept study. To facilitate the required atrial fibrillation functionality, we adopted an established AI model, based on Long Short-Term Memory (LSTM) technology for hardware implementation. The adaptation was done by varying design parameters such as data window and the number of LSTM units. We found that a data window of 40 beats and 20 LSTM units are sufficient to achieve a classification accuracy of 99.02%. We are confident that the A-Cube methodology can be used to implement this model in hardware. Doing so, will create a low power and low latency atrial fibrillation monitoring solution which has the potential to extend the observation duration while being convenient for patients.