A smart detection technology for personal ECG monitoring via chaos-based data mapping strategy
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A smart detection technology for personal ECG monitoring via chaos-based data mapping strategy Shih-Yu Li 1
1
& Yu-Cheng Lin & Lap-Mou Tam
2,3
Received: 4 May 2020 / Revised: 7 September 2020 / Accepted: 17 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This paper presents a smart detection technology for personal Electrocardiography (ECG) monitoring based on data integral-transform of chaotic system. First of all, a set of datafeeding system is developed, ECG data is technically converted into multipledimensional phase space, i.e., the dynamics of ECG data in time domain has been mapped into chaotic domain. Further, some effective and potential features in different sub-dimensional phase plane of the data, such as Euclidean Feature Values (EFV), Central Point Distribution (CPD), are captured, which indicates key biomarkers for different ECG states. In the final stage, following the key biomarkers, explicit boundary thresholds are defined for classification of different ECG states. Three ECG states given via open database-PhysioNet are validated, including normal sinus rhythm (NSR), congestive heart failure (CHF) and sleep apnea (SA). The experimental results show that the developed smart detection technology is effective and feasible for detecting and monitoring the states of such personal ECG states. Keywords Smart machine . Chaos-based data transform . ECG monitoring
1 Introduction Cardiovascular disease (CVD) is one of the leading death causes in the current modern society, which accounts for more than 30% of global death [49]. Therefore, early-detection as well as real-time monitoring of personal heart status become to be relatively important, thereby * Shih-Yu Li [email protected]
1
Graduate Institute of Manufacturing Technology, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd, Taipei 10608, Taiwan
2
Institute for the Development and Quality, Macau, Macao
3
Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau, Macao
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preventing sudden cardiac death (SVD) [34]. Among several approaches for heart detection, Electrocardiography (ECG) discloses the pattern of depolarization and repolarization of the heart muscles during each heartbeat, which is providing effective patterns, convenient easements, and convinced results in the clinical applications, and becoming a promising source for the study of structure and function of the heart due to its low cost, ease of use, high efficiency and non-invasiveness. As more and more personal portable devices are used to obtain ECG data, many ECG records can be collected. However, it is impossible to read and analyze all these data manually by medical professionals. It is best to use a machine to automatically classify heartbeats to help clinicians diagnose heart health. In the past, many studies have developed several computerbased automation tools for heart monitoring based on ECG signals. Heart health detecti
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