HeartSearcher: finds patients with similar arrhythmias based on heartbeat classification

HeartSearcher: finds patients with similar arrhythmias based on heartbeat classification

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles 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
Your details
Why are you recommending this title?
Select reason:
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems of this variety are simply designed to detect arrhythmia through heartbeat classification, and do not provide any additional support for clinical decisions. HeartSearcher identifies patients with similar arrhythmias from heartbeat classifications, by summarising each patient's typical heartbeat pattern in the form of a regular expression, and then ranking patients according to the similarities of their patterns. Results obtained using electrocardiogram data from the MIT-BIH arrhythmia database show that this abstraction reduces the volume of heartbeat classifications by 98% on average, offering great potential to support clinical decisions.


    1. 1)
      • 1. Noureddine, B., Fethi, B.-R.: ‘Bluetooth portable device for ECG and patient motion monitoring’, Nature Technol., 2011, 4, pp. 1923.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 11. Gradl, S., Kugler, P., Lohmuller, C., et al: ‘Real-time ECG monitoring and arrhythmia detection using android-based mobile device’, Proc. Int. Conf. IEEE Engineering in Medicine and Biology Society, San Diego, CA, August–September 2012, pp. 24522455.
    12. 12)
    13. 13)
      • 13. ‘PhysioBank Annotations’,, accessed June 2015.
    14. 14)
      • 14. Association for the Advancement of Medical Instrumentation: ‘Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms’ (ANSI/AAMI EC:57:1998 standard, 1998).
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • 20. Sasikala, P., Wahidabanu, R.S.D.: ‘Identification of individuals using electrocardiogram’, J. Com. Sci. Netw. Secur., 2010, 10, (12), pp. 147153.
    21. 21)
    22. 22)

Related content

This is a required field
Please enter a valid email address