Detail publikace

Precise Pacing Artefact Detection

Originální název

Precise Pacing Artefact Detection

Anglický název

Precise Pacing Artefact Detection

Jazyk

en

Originální abstrakt

Introduction: An analysis of ultra-high-frequencies in ECG (UHF ECG, up to 2 kHz) reveals new information about the time spatial distribution of heart depolarization. Such an analysis may be important for diagnosing and treating patients with atrial and ventricular dyssynchrony. The UHF analysis in patients with a pacing device is complicated due to the pacing influence in the ECG. In that case, all pacing artefacts must be eliminated from the measured signal. The first step in removing those artefacts is to precisely detect their temporal position. Although pacing artefacts are usually clearly visible on a measured ECG, capturing the whole pacing artefact may be challenging. Methods: This paper compares different detection approaches and evaluates them on 19 records. Derivatives, a moving statistical window and complex envelope methods were tested followed by descriptive statistics approaches for making a peak detection. We evaluated the variability of the detection position by the distance variability from manual anotations. For each method, sensitivity and positive predictivity were evaluated. Results: The method with the most precise temporal detection was the variance moving window with a standard deviation (SD) of ±0.11 ms mark placement. The best detection method was a SD moving window with sensitivity=100 and specificity=82.3 and was evaluated as the most appropriate.

Anglický abstrakt

Introduction: An analysis of ultra-high-frequencies in ECG (UHF ECG, up to 2 kHz) reveals new information about the time spatial distribution of heart depolarization. Such an analysis may be important for diagnosing and treating patients with atrial and ventricular dyssynchrony. The UHF analysis in patients with a pacing device is complicated due to the pacing influence in the ECG. In that case, all pacing artefacts must be eliminated from the measured signal. The first step in removing those artefacts is to precisely detect their temporal position. Although pacing artefacts are usually clearly visible on a measured ECG, capturing the whole pacing artefact may be challenging. Methods: This paper compares different detection approaches and evaluates them on 19 records. Derivatives, a moving statistical window and complex envelope methods were tested followed by descriptive statistics approaches for making a peak detection. We evaluated the variability of the detection position by the distance variability from manual anotations. For each method, sensitivity and positive predictivity were evaluated. Results: The method with the most precise temporal detection was the variance moving window with a standard deviation (SD) of ±0.11 ms mark placement. The best detection method was a SD moving window with sensitivity=100 and specificity=82.3 and was evaluated as the most appropriate.

BibTex


@inproceedings{BUT128350,
  author="Juraj {Jurčo} and Filip {Plešinger} and Josef {Halámek} and Pavel {Jurák} and Magdaléna {Matejková} and Jolana {Lipoldová} and Pavel {Leinveber}",
  title="Precise Pacing Artefact Detection",
  annote="Introduction: An analysis of ultra-high-frequencies in
ECG (UHF ECG, up to 2 kHz) reveals new information
about the time spatial distribution of heart depolarization.
Such an analysis may be important for diagnosing and
treating patients with atrial and ventricular dyssynchrony.
The UHF analysis in patients with a pacing device is complicated
due to the pacing influence in the ECG. In that
case, all pacing artefacts must be eliminated from the measured
signal. The first step in removing those artefacts is to
precisely detect their temporal position. Although pacing
artefacts are usually clearly visible on a measured ECG,
capturing the whole pacing artefact may be challenging.
Methods: This paper compares different detection approaches
and evaluates them on 19 records. Derivatives, a
moving statistical window and complex envelope methods
were tested followed by descriptive statistics approaches
for making a peak detection. We evaluated the variability
of the detection position by the distance variability from
manual anotations. For each method, sensitivity and positive
predictivity were evaluated.
Results: The method with the most precise temporal detection
was the variance moving window with a standard
deviation (SD) of ±0.11 ms mark placement. The best
detection method was a SD moving window with sensitivity=100
and specificity=82.3 and was evaluated as the
most appropriate.",
  booktitle="Computing in Cardiology",
  chapter="128350",
  edition="2016",
  howpublished="print",
  year="2016",
  month="september",
  pages="1--4",
  type="conference paper"
}