Key result
The NSGA-II based convolution neural network (N2CNN) achieved an anomaly detection accuracy of 94.83% on the MIT-BIH arrhythmia dataset and 94.96% on the INCART dataset.
Why the study?
Real-time detection of anomalies in remote patient monitoring data is challenging, especially for ECG data.
Does the N2CNN method improve anomaly detection accuracy in ECG data compared to state-of-the-art methods?
Does the N2CNN method improve anomaly detection accuracy in ECG data compared to state-of-the-art methods?
Answers are grounded in this paper and cite their sources.
The proposed NSGA-II based CNN method provides high accuracy for real-time anomaly detection in ECG data, offering a potential tool for remote patient monitoring.
Internet-of-medical-things is the new means of monitoring patient health remotely. However, the real-time detection of anomalies in the patient data is a challenging task, especially on ECG-data. To ease the same, a novel method, NSGA-II based convolution neural network, is presented in this paper for efficient anomaly detection. In the proposed method, non-dominated sorting genetic algorithm-II is emto obtain optimal hyper-parameters of CNN by evaluating three objective functions namely, accuracy, precision, and recall. Further, the performance validation of the proposed method is conducted on two public datasets and compared against seven state-of-the-art methods. Experimental results affirm that the proposed method outperforms the considered methods with an accuracy of 94.83% and 94.96% on MIT-BIH arrhythmia dataset and INCART dataset, respectively. Therefore, it can be claimed that the proposed method is an efficient alternative for anomaly detection.
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Pal et al. (2022) studied Arrhythmia (ECG anomalies). NSGA-II based convolution neural network (N2CNN) vs. Seven state-of-the-art methods was evaluated on Anomaly detection accuracy. The NSGA-II based convolution neural network (N2CNN) achieved an anomaly detection accuracy of 94.83% on the MIT-BIH arrhythmia dataset and 94.96% on the INCART dataset.
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