AR modeling of heart rate signals

Jagadìsh Nayak; P. Subbanna Bhat; Acharya U. Rajendra; U. C. Niranjan; Ong Wai Sing

doi:10.1109/TENCON.2004.1414622

AR modeling of heart rate signals

Jagadìsh Nayak, P. Subbanna Bhat, Acharya U. Rajendra, U. C. Niranjan, Ong Wai Sing

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

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.

Original language	English
Article number	1414622
Pages (from-to)	422-426
Number of pages	5
Journal	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2004-January
DOIs	https://doi.org/10.1109/TENCON.2004.1414622
Publication status	Published - 01-01-2004
Externally published	Yes
Event	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering - Chiang Mai, Thailand Duration: 21-11-2004 → 24-11-2004

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TENCON.2004.1414622

Cite this

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title = "AR modeling of heart rate signals",

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.",

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journal = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

issn = "2159-3442",

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T1 - AR modeling of heart rate signals

AU - Nayak, Jagadìsh

AU - Bhat, P. Subbanna

AU - Rajendra, Acharya U.

AU - Niranjan, U. C.

AU - Sing, Ong Wai

PY - 2004/1/1

Y1 - 2004/1/1

N2 - 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.

AB - 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.

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Y2 - 21 November 2004 through 24 November 2004

ER -

AR modeling of heart rate signals

Abstract

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