Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
AI for Emerging Verticals: Human-robot computing, sensing and networking — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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

Inspec keywords: electrocardiography; health care; learning (artificial intelligence); compressed sensing; Internet of Things; data reduction

Other keywords: classification information; PhysioNet databases; ECG recordings; MIT-BIH Arrhythmia database; Internet of Things; connected health; data reduction; MIT-BIH normal sinus rhythm database; data transmission; BIDMC congestive heart failure database; deterministic measurement matrix; machine learning; deterministic compressed sensing

Subjects: General and management topics; Digital signal processing; Electrical activity in neurophysiological processes; Biology and medical computing; Textbooks; Ubiquitous and pervasive computing; Bioelectric signals; Knowledge engineering techniques; Data handling techniques; General electrical engineering topics

Preview this chapter:
Zoom in
Zoomout

Combining deterministic compressed sensing and machine learning for data reduction in connected health, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc034e/PBPC034E_ch15-1.gif /docserver/preview/fulltext/books/pc/pbpc034e/PBPC034E_ch15-2.gif

Related content

content/books/10.1049/pbpc034e_ch15
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address