## 4 Discussion  
This study provides positive evidence for deploying deep neural networks on conventional microcontrollers, considering that DNNs have the potential to improve rhythm analysis for ventricular fibrillation detection in stand-alone AED devices. By implementing a quantization strategy, we enable low-resource microcontrollers to execute deep CNN models, achieving accurate shock-advisory results on cardiac arrest ECG data (Se = 98.6%, Sp = 99.5%) with internal computation latencies that can be reduced from 4.8 s to 0.6 s, thereby ensuring real-time operational capability. While numerous advanced deep CNN models have shown excellent performance in rhythm classification, particularly when prospectively tested on OHCA databases using powerful GPU-based development platforms [65; 66; 67; 68; 69; 70; 71; 72; 73], such analyses are often restricted to offline operating modes. Although effective in controlled environments, these DNN models are typically regarded as impractical for real-life clinical applications due to their reliance on high computational resources and limited consideration of the constraints imposed by embedded systems. This study shows that deep learning models can run on limited-resource hardware, opening the door for their integration into real-world AED devices. Particularly, we investigate two popular types of ARM-based microcontrollers (Raspberry  
Figure 11: Examples of 10 s ECG strips with inverse classification outcomes of TF DNN (reference) vs. quantized LiteRT DNN (IntQ on left and Full-IntQ on right) based on a decision threshold pVF = 0.5. The cases are highlighted in Figure 10.  
Pi 4 and STM32F7), which feature high and low levels of computational capabilities, respectively, that can be of certain interest to different developers. The powerful Raspberry Pi 4 microcontroller is fully supported by embedded software libraries in Python and TensorFlow Lite, allowing it to directly execute converted LiteRT models without the need for additional design efforts, such as post-training quantization or addressing performance degradation concerns. In our tests, a directly converted LiteRT DNN running on the Raspberry Pi 4 replicates the accuracy of the reference TF DNN model (Table 4) and achieves a fast and highly reproducible inference time of just 1.064 \(\pm\) 0.025 ms, comparable to the execution time on the GPU-based development platform (Table 3). While the Raspberry Pi 4 demonstrates significant advantages for running DNNs, several limitations might be considered when evaluating its suitability for integration into AEDs--compact, battery-operated devices that demand high reliability during emergencies and extended standby periods. These limitations include high power consumption (2.25 W in idle mode, 3.67 W during DNN model execution); large physical size; need for cooling; a Linux operating system not optimized for responsiveness during critical situations; additional protective measures required to withstand extreme temperatures, humidity, and vibrations; and increased costs for peripheral components and adaptations to meet medical device compliance and stringent regulatory standards for AEDs. For these reasons, simpler, low-power, real-time microcontrollers, such as STM32 or similar ARM-based chips are typically preferred in AED designs. The low-power STM32F7 microcontroller running the same LiteRT DNN model as Raspberry Pi 4 in this study presented considerable inference latency of over 4.8 s (Table 3), which adds almost 50% to the 10 s data collection period, limiting real-time applications. This underscores that running DNNs on the STM32F7 microcontroller without reducing computational complexity is not viable for real-time processing. We found that simple dynamic range quantization, which does not require external resources such as a calibration dataset, is not applicable to the STM32F7 microcontroller. This limitation arises because the current version of the LiteRT library for microcontrollers does not support hybrid models.