Abstract
Heart rate variability (HRV) analysis begins with the
relatively non-invasive and easily obtained process of
ECG recording, yet provides a wealth of information on
cardiovascular health. Measures obtained from HRV use
time-domain, frequency-domain and non-linear
approaches. These measures can be used to detect
disease, yet from the large number of possible measures,
it is difficult to know which to select, in order to provide
the best separation between disease and health.
This work reports on a case study using a variety of
measures to detect the early stages of Cardiac Autonomic
Neuropathy (CAN), a disease that affects the correct
operation of the heart and in turn leads to associated co
morbidities. We examined time- and frequency-domain
measures, and also non-linear measures. In all, 80
variables were extractedfrom the RR interval time series.
We applied machine learning methods to separate
participants with early CAN from healthy aged-matched
controls, while using a Genetic Algorithm to search for
the subset of measures that provided the maximum
separation between these two classes. Using this subset
the best performance was an accuracy of 70% achieved
on unseen data.
relatively non-invasive and easily obtained process of
ECG recording, yet provides a wealth of information on
cardiovascular health. Measures obtained from HRV use
time-domain, frequency-domain and non-linear
approaches. These measures can be used to detect
disease, yet from the large number of possible measures,
it is difficult to know which to select, in order to provide
the best separation between disease and health.
This work reports on a case study using a variety of
measures to detect the early stages of Cardiac Autonomic
Neuropathy (CAN), a disease that affects the correct
operation of the heart and in turn leads to associated co
morbidities. We examined time- and frequency-domain
measures, and also non-linear measures. In all, 80
variables were extractedfrom the RR interval time series.
We applied machine learning methods to separate
participants with early CAN from healthy aged-matched
controls, while using a Genetic Algorithm to search for
the subset of measures that provided the maximum
separation between these two classes. Using this subset
the best performance was an accuracy of 70% achieved
on unseen data.
| Original language | English |
|---|---|
| Title of host publication | Computing in Cardiology 2014 |
| Subtitle of host publication | Volume 41 |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 93-96 |
| Number of pages | 4 |
| Volume | 41 |
| ISBN (Electronic) | 9781479943470 |
| ISBN (Print) | 9781479943463 |
| Publication status | Published - 2014 |
| Event | 41st Computing in Cardiology Conference, CinC 2014 - MIT's Laboratory for Computational Physiology, Cambridge, United States Duration: 07 Sept 2014 → 10 Sept 2014 http://www.cinc.org/2014/ (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7035785 (Conference website) |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 2325-8861 |
Conference
| Conference | 41st Computing in Cardiology Conference, CinC 2014 |
|---|---|
| Country/Territory | United States |
| City | Cambridge |
| Period | 07/09/14 → 10/09/14 |
| Other | Computing in Cardiology provides an international forum for scientists and professionals from the fields of medicine, physics, engineering and computer science, and has been held annually since 1974. |
| Internet address |
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