This paper introduces a novel method for the 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 the identification of apnea subjects. This process is complex, expensive, and time-consuming. In this paper, both online and off-line-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, 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 off-line processes are 97.5% and 95%, respectively. © 2001-2012 IEEE.

Suchetha M

Electronics

School of Electronics Engineering

Chennai Campus

Smruthy A

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Real-Time Classification of Healthy and Apnea Subjects Using ECG Signals With Variational Mode Decomposition

IEEE Sensors Journal

To enhance the performance and sensitivity of continuous monitoring systems for detection of chronic diseases, selection of optimal machine learning algorithms is pivotal. Presently, the commonly used algorithms face constraints, such as high computational cost and lack of optimal feature selection on application to real time signals thereby reducing the efficiency of such analysis. Deep learning approaches, such as the convolution neural network, overcome these drawbacks by calculating automated features from raw signal and classifying the derived features. This architecture shows good merits. However, the use of fully connected multi-layer perceptron algorithms have shown low classification performance. This paper proposes to develop a modified deep learning convolution neural network algorithm integrated with support vector machines to address the drawbacks present in multi-layer perceptron and thereby improving the overall performance of real-time detection applications. The system is validated on real-time breath signals for non-invasive detection of diabetes. The performance of this proposed algorithm is evaluated and compared with the existing technique. © 2017 IEEE.

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An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals

Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

Circuits, Systems, and Signal Processing

A New ECG Signal Classification Based on WPD and ApEn Feature Extraction

IEEE Journal of Emerging and Selected Topics in Power Electronics

Single-Phase Grid Voltage Frequency Estimation Using Teager Energy Operator-Based Technique

Journal	Data powered by TypesetIEEE Sensors Journal
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers (IEEE)
ISSN	1530-437X
Open Access	0