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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Nayak, J. | |
| dc.contributor.author | Bhat, P.S. | |
| dc.contributor.author | Rajendra, A.U. | |
| dc.contributor.author | Niranjan, U.C. | |
| dc.contributor.author | Sing, O.W. | |
| dc.date.accessioned | 2020-03-30T09:58:58Z | - |
| dc.date.available | 2020-03-30T09:58:58Z | - |
| dc.date.issued | 2004 | |
| dc.identifier.citation | IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2004, Vol.2004-January, , pp.422-426 | en_US |
| dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/7382 | - |
| dc.description.abstract | The electrocardiogram (ECG) is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks etc may contain useful information about the nature of disease afflicting the heart. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal is used as the base signal for the highly useful in diagnostics. This paper deals with the analysis of eight cardiac abnormalities using Auto Regressive (AR), modeling technique. The results are tabulated below for specific example. � 2004 IEEE. | en_US |
| dc.title | AR modeling of heart rate signals | en_US |
| dc.type | Book chapter | en_US |
| Appears in Collections: | 2. Conference Papers | |
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