Key result
The AAC-FFAHDL system achieved promising performance for automated arrhythmia classification using ECG signals compared with other models.
Why the study?
Manual diagnosis and classification of arrhythmia from ECG signals is challenging and time-consuming, limiting the potential benefits of IoT-based continuous monitoring.
Does the AAC-FFAHDL approach improve automated arrhythmia classification performance compared to other models using benchmark ECG databases?
Does the AAC-FFAHDL approach improve automated arrhythmia classification performance compared to other models using benchmark ECG databases?
Answers are grounded in this paper and cite their sources.
The proposed AAC-FFAHDL deep learning model demonstrates promising performance for automated arrhythmia classification from ECG signals, potentially facilitating remote patient monitoring in IoT environments.
In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.
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Almasoud et al. (2023) studied Arrhythmia. Automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) vs. Other models was evaluated on Classification and detection performance. The AAC-FFAHDL system achieved promising performance for automated arrhythmia classification using ECG signals compared with other models.
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