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## Combining deterministic compressed sensing and machine learning for data reduction in connected health

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Connected health is continuously developing, particularly with the advent of the Internet of Things (IoT) interconnecting various sensing nodes capable of measuring a person's vital signs such as electrocardiogram (ECG). In the years to come, the current forecasts indicate a significant increase in demand of such devices, especially among a currently underserved but significant population. Most of the existing devices performing measurement and data transmission require significant effort to integrate more intelligent processing or even decision-making, at least for data reduction and more autonomy. In this chapter, we propose to combine a simple compressed sensing (CS) measurement technique with a machine learning classification, both for data reduction and low power consumption. The classification is performed on compressed data, whereas the transmission is achieved only for warnings, by sending classification information in the case of a probable pathology detection, and if neces-sary the compressed data for further analysis. For data acquisition, we utilize a simple deterministic measurement matrix that facilitates the hardware implementation. The performance of the proposed approach is demonstrated using ECG recordings from three PhysioNet databases: MIT-BIH Arrhythmia Database, MIT-BIH Normal Sinus Rhythm Database and The BIDMC Congestive Heart Failure Database.

Chapter Contents:

• 15.1 Introduction
• 15.2 Background and related work
• 15.2.1 Compressive sensing
• 15.2.2 Classification in compressed domain
• 15.3 Method
• 15.3.1 Compressive sensing acquisition
• 15.3.2 Feature extraction
• 15.3.2.1 Auto-regressive (AR) model
• 15.3.2.2 Shannon entropy
• 15.3.2.3 Multifractal (MF) wavelet
• 15.3.2.4 Feature vector
• 15.3.3 Classification
• 15.4 Experimental results and discussion
• 15.4.1 Datasets
• 15.4.2 Training and validation results
• 15.5 Conclusion
• References

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