## 3 Results
### Accuracy Metrics  
Table 4 presents the shock advisory performance of all DNN models for the detection of four rhythm categories, evaluated in terms of Sp (NSR, OR, Asystole) and Se (VF) on the test dataset. The accuracies presented in the first column correspond to the original TF DNN model tested on the GPU-based platform, which we found to be identical to those of the non-quantized LiteRT DNN models running on both the Raspberry Pi 4 and STM32F7 microcontroller boards. This is due to the fact that non-quantized LiteRT models retain full model precision. After quantization, a slight increase in Sp (NSR) is observed for the IntQ model and in Sp (ASYS, ONR) for the Full-IntQ models, while Se (VF) remains consistent across all tested models. The performance presented in Table 4 can be further analyzed in detail in Figure 10, which shows the DNN output observations, specifically pVF, for the test dataset. As the regression scatterplots represent overlapping data points, the histograms are important to show the real data distributions (number of observations). The histograms for ventricular fibrillation reveal that the quantization limits the maximal pVF range to 0.95 (IntQ model) and 0.9 (Full-IntQ model). Nevertheless, this has no effect on performance, given that a rhythm is classified as ventricular fibrillation at a threshold of pVF \(\geq\) 0.5. Additionally, the scatterplots in Figure 10 highlight all cases where the decisions of the original TF DNN model differ from those of the quantized LiteRT DNN models (IntQ and Full-IntQ). The analysis of the inverse decisions of the IntQ model (Figure 10, top) is as follows:  
* NSR--A single TN decision differs from the TF DNN model's decision, resulting in a 0.4% increase in Sp for NSR. * ONR--One TN and one FP decision differ from the TF DNN model's output. These inverse decisions balance each other, leaving the Sp for ONR unchanged. * ASYS--Two TN and two FP decisions diverge from the TF DNN model's output. As with ONR, these inverse decisions balance each other, keeping the Sp for ASYS unchanged. * VF--No inverse decisions are observed, and Se remains unchanged. The analysis of the inverse decisions of the Full-IntQ model (Figure 10, bottom) is as follows:  
* NSR--No inverse decisions are observed, and Sp remains unchanged;
* ONR--Two TN decisions deviate from the TF DNN model's output, resulting in a 0.1% increase in Sp for ONR;
* ASYS--Nine TN decisions deviate from the TF DNN model's output, contributing to a 0.2% increase in Sp for ONR;
* VF--No inverse decisions are observed, and Se remains unchanged. \begin{table}
\begin{tabular}{c c c c} \hline  & **TF DNN (Reference)** & \multicolumn{2}{c}{**LiteRT DNN (Quantized)**} \\  & **LiteRT DNN** & **IntQ** & **Full-IntQ** \\  & **(Non-quantized)** & & \\ \hline Normal sinus rhythm (Specificity) & 99.60\% (246/247) & 100\% (247/247) & 99.60\% (246/247) \\ Other non-shockable rhythms (Specificity) & 98.85\% (2241/2267) & 98.85\% (2241/2267) & 98.94\% (2243/2267) \\ Asystole (Specificity) & 99.54\% (4533/4554) & 99.54\% (4533/4554) & 99.74\% (4542/4554) \\ Ventricular fibrillation (Sensitivity) & 98.55\% (408/414) & 98.55\% (408/414) & 98.55\% (408/414) \\ \hline \end{tabular}
\end{table} 
Table 4: Test accuracy performance of all DNN models investigated in this study, according to the test scheme in Figure 5. The accuracy of the TF DNN model (reference) and non-quantized LiteRT DNN models running on Raspberry Pi 4 and STM32F7 microcontroller boards are shown in the first column, yielding identical results. The results for the quantized LiteRT DNN models (IntQ and Full-IntQ) running on the STM32F7 microcontroller are presented in the second and third columns. Figure 11 presents an overview of representative signals that result in inversed classification outcomes for the quantized LiteRT DNN (IntQ and Full-IntQ) compared to the reference TF DNN model. These signals often exhibit complex, non-trivial rhythms, sometimes accompanied by artifacts, which pose challenges for any VF detection algorithm.