## 5 Conclusions  
An energy-efficient cardiac arrhythmia detection ECG processor based on HDWT and hybrid classifiers is presented in this paper. We first optimize the Hilbert-based R-peak detection algorithm to make it easier to implement in hardware. Next, using the optimized R-peak detection engine, the ECG data are decomposed into segments with 128 sample points. Then, 16-dimensional feature vectors are extracted by calculating the level-3 HDWT approximation coefficient. Finally, the feature vectors are transmitted to the hybrid classifier for classification. The hybrid classifier combines a linear pre-classifier and a polynomial kernel SVM to achieve high accuracy and low complexity. The experimental results demonstrate that the R-peak extraction method based on the Hilbert transform outperforms the traditional ones in terms of accuracy. Moreover, the hybrid classifier SVM classification using the approximate coefficients derived from HDWT as feature vectors exhibits satisfactory classification performance. The proposed algorithm and hardware are built on the MIT_BIT database, and the classification accuracy is usually affected by large patient-to-patient variations. Therefore, future work will focus on developing an adaptive learning engine to learn the user's signal features and improve classification accuracy. In addition, to address the storage and analysis needs of large-scale ECG datasets, we will develop a context-aware compression engine. This engine would dynamically adjust compression algorithms based on heartbeat classification. Typical beats would undergo lossy compression for efficient storage, while abnormal beats would be subjected to lossless compression to preserve diagnostic information for further investigation.  
Conceptualization, J.D., X.W. and X.Z.; methodology, J.D. and J.M.; software, J.D. and J.Y.; validation, S.L. and H.C.; data curation, J.D. and J.M.; writing--original draft preparation, J.D. and J.Y.; writing--review and editing, H.C. and S.L.; visualization, J.D. and J.Y.; supervision, X.W. and X.Z.; project administration, X.W. and X.Z.; funding acquisition, X.W. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a fundamental research grant from the Shenzhen Science and Technology Innovation Commission, grant number KQTD20200820113105004.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank [PERSON] for his beneficial suggestions and comments.  
Authors [PERSON] and [PERSON] were employed by the company Shanghai BU (Xishuo Microelectronic) of RUNIC Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Method** & **Technology (nm)** & **Area (mm\({}^{2}\))** & **Accuracy (\%)** \\ \hline
[48] & ELM & 40 & 0.2123 & 92 \\ \hline
[49] & Threshold-based & 180 & NA & 97.02 \\ \hline
[50] & Machine Learning & 90 & 4.989 & 95.8 \\ \hline
[8] & Naïve Bayes & 65 & 0.112 & 86 \\ \hline This work & SVM & 110 & 1.34 & 97.8 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison with previous works.