Distilled Deep Learning based Classification of Abnormal Heartbeat Using ECG Data through a Low Cost Edge Device To meet the accuracy, latency and energy efficiency requirements of modern healthcare systems during realtime collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern AI techniques. Using the stateoftheart machine learning based classification techniques plays a crucial role in creating the optimal healthcare system on the edge. This work first provides a background on the current and emerging edge computing classification techniques for healthcare applications, specifically for electrocardiogram (ECG) beat classification. After implementing these classification techniques on a Raspberry Pi based platform we perform a comparison of the performance of these classification techniques with respect to three key performance indicators (KPI) of interest for health care applications namely accuracy, energy efficiency, and latency. Benefiting from the results of the comparative analysis presented in this work, a distilled neural network algorithm can be selected for optimal deployment and over 90% accuracy in given scenario in healthcare system depending on the specific requirements of the given scenario.

Fusing Transformer Model with Temporal Features for ECG Heartbeat Classification ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. Traditional heartbeat classification methods rely on handcrafted features and often fail to learn potentially abstract patterns, while current deep learning based methods usually consist of complex convolutional and recursive structures. In this paper, considering the time sequence property of ECG signals, we propose a novel heartbeat classification method based on Transformer, a sequencetosequence model with a relatively simple architecture and higher degree of parallelism. To adapt Transformer to our problem, we use only the encoder part of the model for that ECG signals do not have translation signals. We also replace the dropout layers with batch normalization layers, considering our smallsize feature space and the natural differences among patients. Further, we fuse handcrafted temporal features with the features learnt by Transformer to better capture rhythmic patterns in ECG signals. We have conducted extensive experiments on the MITBIH arrhythmia database using both the original dataset and an augmented dataset with more balanced data. The results show that our model can achieve 99.62% accuracy, 95.09% precision and 94.12% sensitivity on the original dataset and 99.87% accuracy, 99.74% precision and 99.74% sensitivity on the augmented dataset. Besides, we have performed multiple experiments against stateoftheart methods using their assessment strategies. Experimental results indicate that our model can achieve better performance under most circumstances.