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Smart Machine & AI Tech. 3
Smart Machine & AI Tech. 3
This research provides a smart detection technology for personal Electrocardiography (ECG) monitoring based on data integral-transform of chaotic system. First of all, a set of data-feeding system is developed, ECG data is technically converted into multiple-dimensional 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.
Fig. 1 The configurations of smart detection technology for personal ECG monitoring
In summary, the evaluation of accuracy rate for those potential features, CPD(x), CPD(y), CPD(z), and EFV, are acceptalbe. It is clear that the proposed smart detection approach is effective, NSR, CHF and SA can be detected in successful via using the features CPD(x), CPD(y), CPD(z), and the experimental results indicate that the EFV cannot be used in detecting NSR and CHF due to range-overlapping. As a consequence, for the detection of the three different ECG states, CPD provide a more acceptable performance to identify the label of ECG states. Further, the developed detection technology is effective, the structure is simple and easy to build up, and can be applied to different research aspect. Also, in fact, there are still many other way to capture related potential features from the transformed data, the related performance as well as feasibility can be investigated in the following research works.
Related Research Achievements:
Shih-Yu Li*, Yu-Cheng Lin, Lap-Mou Tam, "A smart detection technology for personal ECG monitoring via chaos-based data mapping strategy", Multimedia Tools and Applications, vol. 80, pp. 6397–6412, Feb. 2021 (SCI, IF= 2.757, Rank: 12/61=19.67%, Q1). -- International Cooperation
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