Key result
A lightweight 2D convolutional neural network deployed on edge devices achieved an overall accuracy of 95.3%, mean sensitivity of 95.27%, and ROC-AUC of 0.9934 for detecting cardiac arrhythmias.
Why the study?
Timely and accurate detection of cardiac arrhythmias is critical to prevent life-threatening complications, particularly where access to cardiologists is limited, such as in emergency departments.
Does a 2D-CNN deployed on edge devices accurately detect cardiac arrhythmias from ECG signals?
Does a 2D-CNN deployed on edge devices accurately detect cardiac arrhythmias from ECG signals?
A lightweight 2D-CNN deployed on edge devices demonstrates high accuracy and speed for real-time cardiac arrhythmia detection from ECG signals.
May support arrhythmia screening in EDs with limited specialist access; leaves open need for prospective clinical validation.
Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3%, mean sensitivity of 95.27%, mean specificity of 98.82%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA® Jetson Nano™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios.
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Seitanidis et al. (2022) studied Cardiac arrhythmias. 2D convolutional neural network (CNN) classifier was evaluated on Overall accuracy for arrhythmia detection. A lightweight 2D convolutional neural network deployed on edge devices achieved an overall accuracy of 95.3%, mean sensitivity of 95.27%, and ROC-AUC of 0.9934 for detecting cardiac arrhythmias.
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