Key result
The proposed AdaBoost kernel support vector machine-based remora optimization algorithm was developed to analyze ECG signals from IoT wearable devices in a fog environment to minimize latency.
Why the study?
Analyzing ECG signals using cloud-based techniques has high latency, while fog-based approaches have minimal computing capability, prompting the need for low-latency, accurate ECG monitoring in arrhythmia patients.
Does the AKSVM-RO algorithm improve the accuracy and reduce the latency of ECG signal analysis for arrhythmia monitoring in an IoT-fog environment compared to other approaches?
Does the AKSVM-RO algorithm improve the accuracy and reduce the latency of ECG signal analysis for arrhythmia monitoring in an IoT-fog environment compared to other approaches?
A novel machine learning algorithm (AKSVM-RO) deployed in a fog computing environment may improve the speed and accuracy of remote ECG monitoring for arrhythmia patients.
May enable faster remote ECG monitoring in fog environments; hypothesis-generating and requires prospective validation before clinical use.
Summary In general, heart disease is considered as the most severe disease that results in high mortality rate globally. An arrhythmia is one type of heart disease caused due to asymmetrical heartbeat rhythm rate (i.e., heartbeat will be too slow or too fast). The generation of IoT devices as well as ECG signals is capable of tracking the heart rhythm of the patients consistently. Even though, analyzing ECG signals using cloud‐based techniques achieves a certain accuracy level with high latency. Then by evaluating the ECG signals with fog‐based approaches minimizes the latency but the computing capability is minimum. Therefore, wearable devices and fog devices are linked directly to attain high service quality with minimum latency. To conquer such drawbacks, this article proposes a novel AdaBoost kernel support vector machine‐based remora optimization (AKSVM based RO) algorithm to analyze the ECG signals and to initiate the crisis with minimum time delay. The proposed approach predicts the ECG signals of arrhythmia affected persons from an IoT wearable device. In addition to this, the proposed AKSVM‐RO approach is more feasible to deploy in fog environment. Also, it assists in optimizing the time delay in calling the ambulance service during emergency situations. Finally, the experimental investigations are conducted for various approaches with respect to simulation measures to minimize the latency and accuracy of the proposed system.
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M. Hemalatha (2022) studied Arrhythmia. AdaBoost kernel support vector machine-based remora optimization (AKSVM-RO) algorithm vs. Various other approaches was evaluated on Latency and accuracy of ECG signal prediction. The proposed AdaBoost kernel support vector machine-based remora optimization algorithm was developed to analyze ECG signals from IoT wearable devices in a fog environment to minimize latency.
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