### Abstract

An accurate estimation of ventricular fibrillation (VF) duration could be critical in selecting the most effective therapeutic intervention. We test the hypothesis that changes in frequency content of VF signals can be quantified by using autoregressive (AR) modeling, and the duration since the onset of VF can be estimated by using this method. VF signals were recorded for up to 300 s in five isolated rabbit hearts. Fourth-order AR parameters of successive segments were estimated, and frequencies of the first poles (the pole with lower frequency) were pooled together and a curve was fitted: F(t) = A exp (-α_{1}) + B, where F(t) is the estimated frequency of the first pole at t'th time instant, α is the decay constant, B is the offset frequency, and A is the frequency at time zero minus the offset frequency. The utility of this curve in estimating the VF duration was tested in four new experiments, and the difference between the actual and the estimated VF duration (estimation error) was calculated. F(t), the frequency of the first pole, decreased from 12 to 6 Hz with duration of VF, while the frequency of the other pole decreased from 25 to 20 Hz. Parameters of the fitted curve were calculated as A = 7.8, o = 0.0041 and B was selected as four. Testing on a new set of VF signals produced very little estimation error for the first 100 s of VF, although this error increased with VF duration. For these new signals, the mean value of the absolute estimation error was 26 s. Results of this study show that changes in the frequency content of electrocardiogram (ECG) during VF can be quantified by AR modeling and that the frequency changes associated with a pole of this model can be used to estimate the VF duration.

Original language | English (US) |
---|---|

Pages (from-to) | 349-356 |

Number of pages | 8 |

Journal | IEEE Transactions on Biomedical Engineering |

Volume | 44 |

Issue number | 5 |

DOIs | |

State | Published - 1997 |

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### Keywords

- Autoregressive processes
- biomedical signal processing
- electrocardiography
- signal detection
- ventricular fibrillation

### ASJC Scopus subject areas

- Biomedical Engineering

### Cite this

*IEEE Transactions on Biomedical Engineering*,

*44*(5), 349-356. https://doi.org/10.1109/10.568910

**Estimation of the ventricular fibrillation duration by autoregressive modeling.** / Baykal, Ahmet; Ranjan, Ravi; Thakor, Nitish V.

Research output: Contribution to journal › Article

*IEEE Transactions on Biomedical Engineering*, vol. 44, no. 5, pp. 349-356. https://doi.org/10.1109/10.568910

}

TY - JOUR

T1 - Estimation of the ventricular fibrillation duration by autoregressive modeling

AU - Baykal, Ahmet

AU - Ranjan, Ravi

AU - Thakor, Nitish V

PY - 1997

Y1 - 1997

N2 - An accurate estimation of ventricular fibrillation (VF) duration could be critical in selecting the most effective therapeutic intervention. We test the hypothesis that changes in frequency content of VF signals can be quantified by using autoregressive (AR) modeling, and the duration since the onset of VF can be estimated by using this method. VF signals were recorded for up to 300 s in five isolated rabbit hearts. Fourth-order AR parameters of successive segments were estimated, and frequencies of the first poles (the pole with lower frequency) were pooled together and a curve was fitted: F(t) = A exp (-α1) + B, where F(t) is the estimated frequency of the first pole at t'th time instant, α is the decay constant, B is the offset frequency, and A is the frequency at time zero minus the offset frequency. The utility of this curve in estimating the VF duration was tested in four new experiments, and the difference between the actual and the estimated VF duration (estimation error) was calculated. F(t), the frequency of the first pole, decreased from 12 to 6 Hz with duration of VF, while the frequency of the other pole decreased from 25 to 20 Hz. Parameters of the fitted curve were calculated as A = 7.8, o = 0.0041 and B was selected as four. Testing on a new set of VF signals produced very little estimation error for the first 100 s of VF, although this error increased with VF duration. For these new signals, the mean value of the absolute estimation error was 26 s. Results of this study show that changes in the frequency content of electrocardiogram (ECG) during VF can be quantified by AR modeling and that the frequency changes associated with a pole of this model can be used to estimate the VF duration.

AB - An accurate estimation of ventricular fibrillation (VF) duration could be critical in selecting the most effective therapeutic intervention. We test the hypothesis that changes in frequency content of VF signals can be quantified by using autoregressive (AR) modeling, and the duration since the onset of VF can be estimated by using this method. VF signals were recorded for up to 300 s in five isolated rabbit hearts. Fourth-order AR parameters of successive segments were estimated, and frequencies of the first poles (the pole with lower frequency) were pooled together and a curve was fitted: F(t) = A exp (-α1) + B, where F(t) is the estimated frequency of the first pole at t'th time instant, α is the decay constant, B is the offset frequency, and A is the frequency at time zero minus the offset frequency. The utility of this curve in estimating the VF duration was tested in four new experiments, and the difference between the actual and the estimated VF duration (estimation error) was calculated. F(t), the frequency of the first pole, decreased from 12 to 6 Hz with duration of VF, while the frequency of the other pole decreased from 25 to 20 Hz. Parameters of the fitted curve were calculated as A = 7.8, o = 0.0041 and B was selected as four. Testing on a new set of VF signals produced very little estimation error for the first 100 s of VF, although this error increased with VF duration. For these new signals, the mean value of the absolute estimation error was 26 s. Results of this study show that changes in the frequency content of electrocardiogram (ECG) during VF can be quantified by AR modeling and that the frequency changes associated with a pole of this model can be used to estimate the VF duration.

KW - Autoregressive processes

KW - biomedical signal processing

KW - electrocardiography

KW - signal detection

KW - ventricular fibrillation

UR - http://www.scopus.com/inward/record.url?scp=0030896055&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030896055&partnerID=8YFLogxK

U2 - 10.1109/10.568910

DO - 10.1109/10.568910

M3 - Article

C2 - 9125819

AN - SCOPUS:0030896055

VL - 44

SP - 349

EP - 356

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 5

ER -