Real-Time Classification of Healthy and Apnea Subjects using ECG Signals with Variational Mode Decomposition
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An Energy Efficient Cross-Layer Network Operation Model for IEEE 802.15.4-Based Mobile Wireless Sensor Networks
Abstract-In thr first Place, introduces a novel method for classification of healthy and apnea subjects using variational mode decomposition. The proposed method distinguishes the apnea and normal subjects with the help of an Electrocardiogram (ECG) signal. Polysomnogram is the gold standard used for identification of apnea subjects. This process is complex, expensive and time consuming. In this paper both online and offline based feature extraction and classification methods are explored. The proper extraction of suitable features from the signal is done by applying variational mode decomposition. Two features are extracted from the variational mode functions (VMFs) namely energy and RR interval of ECG signal. These features are fed in to a support vector machine classifier where they are classified as healthy and apnea. The accuracy obtained for both online and offline processes are 97.5% and 95% respectively.
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