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Real time QRS complex detection using DFA and regular grammar.

Salah Hamdi1, Asma Ben Abdallah2, Mohamed Hedi Bedoui2

  • 1Laboratory of Technology and Medical Imaging (LTIM), Faculty of Medicine of Monastir (FMM), University of Monastir, Monastir, Tunisia. salahxhamdi@gmail.com.

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|March 1, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using deterministic finite automata and regular grammar for accurate QRS complex detection in electrocardiogram (ECG) signals. The approach achieves high sensitivity and specificity, outperforming traditional statistical methods.

Keywords:
DFAECG signalQRS complexRR distanceRegular grammar

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate QRS complex detection is vital for electrocardiogram (ECG) analysis.
  • Existing methods may require refinement for optimal performance.

Purpose of the Study:

  • To develop a novel QRS complex detection method using deterministic finite automata and regular grammar.
  • To validate the efficacy of this approach on standard ECG datasets.

Main Methods:

  • A new QRS detection algorithm based on deterministic finite automata with added constraints.
  • Utilizing regular grammar for QRS complex extraction and interpretation of normalized ECG signals.
  • Defining QRS complexes as pairs of adjacent peaks meeting specific duration and standard deviation criteria.

Main Results:

  • The method was tested on 48 ECG signals from the MIT-BIH arrhythmia database.
  • Achieved high performance metrics: 99.74% sensitivity and 99.86% specificity.
  • Analyzed the impact of Signal-to-Noise Ratio (SNR) on detection accuracy.

Conclusions:

  • Deterministic automata and regular grammar are effective for ECG signal diagnosis.
  • The grammar-based approach yields competitive and satisfactory results compared to statistical methods.
  • The proposed method offers comparable or superior performance to existing literature findings.