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.

Atrial Fibrillation (AF) is a common heart rhythm disorder which increases the stroke risk fivefold . It is estimated that AF prevalence in the UK is around 1% of the general population and increases to 10% within the older population. AF is diagnosed by analysing the electrocardiogram (ECG) of the human heart. Figure 1 shows an ECG signal before and during an AF episode.   This analysis can be done manually, which is labour intensive or with artificial intelligence (AI) models.  Currently the AI models are executed in cloud servers and the data must travel from the point of measurement to that server over a network. Communicating the data requires network connectivity and energy expenditure associated with accessing the network. Furthermore, communication channels pose data safety and security issues. Moreover, a setup procedure is needed for transferring patient data to the cloud server. These systemic issues limit the observation duration of measurement equipment, such as patch sensors for heart rate capturing. The reduced observation duration will reduce the number of observable AF episodes which has a negative impact on AF diagnosis.  

The following graph represents the performance of the model based on quantization.

The following image represent ECG recording before and during an atrial fibrillation episode. this analysis may be performed manually, which is labour intensive or with Artificial intelligence models. currently , the artificial intelligence models are executed in cloud servers and data must travel from the point of measurement to that server over a network. the data shown in the image is from the MIT_BIH Arrhythmia Database. available from MIT-BIH Arrhythmia Database v1.0.0 (physionet.org)

our current work is focusing on FPGA verification by determining the correct location for the memory contents files. 

these files are in the correct location for simulation but not for synthesis.

we have completed the bus interface and currently working on completing the FPGA verification by determining the correct location for the memory contents files.

our current focus is on creating console output from the FPGA implementation which will determine how long the FPGA verification takes

the console output from the FPGA implementation of the Atrial Fibrillation Detection core