Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 1, 2019International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringOpen Access

Detection of electrocardiogram QRS complex based on modified adaptive threshold

View Full Paper
Ask AI
Bookmark
Share

Key result

Modified adaptive threshold algorithm achieves ~99.6% sensitivity for QRS complex detection.

  • n=48

Why the study?

Analyzing ECG signals is essential for medical diagnoses, with QRS complex detection serving as the core of this analysis.

Does a modified adaptive threshold method accurately detect QRS complexes in ECG signals?

Comparison

Modified adaptive threshold method based on statistical analysis

Authors

EHEhab AbdulRazzaq HusseinASAli ShabanHAHilal Al-Libawy

Discussion

Loading...

Member takes

Overview

May support ECG algorithm research; leaves open prospective clinical validation before any practice change.

Structured PICO

Does a modified adaptive threshold method accurately detect QRS complexes in ECG signals?

P
Population
48 ECG records containing 44,715 heartbeats from the MIT-BIH arrhythmia database used to validate a QRS detection algorithm.
I
Intervention
Modified adaptive threshold method based on statistical analysis of the signal for QRS complex detection
O
Outcome
QRS complex detection performance (sensitivity and positive predictivity)surrogate

A modified adaptive threshold method based on statistical analysis demonstrates high sensitivity and positive predictivity for QRS complex detection in ECG signals.

Limitations

  • Not all records were detected properly (e.g., record 228) due to negative QRS polarities and ventricular ectopics.

Cite This Study

Hussein et al. (2019) studied Arrhythmia (n=48). Modified adaptive threshold algorithm vs. State-of-the-art QRS detection algorithms was evaluated on QRS complex detection sensitivity. The modified adaptive threshold algorithm achieved high QRS complex detection performance with a sensitivity of 99.62% and a positive predictivity of 99.88% on the MIT-BIH arrhythmia database.

synapsesocial.com/papers/6aa4d81deaa8e1ec0014275bhttps://doi.org/10.11591/ijece.v9i5.pp3512-3521
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Adaptive threshold QRS detection algorithm for ambulatory ECG2002 · 17 citations
  2. 2A QRS complex detection algorithm using electrocardiogram leads2003 · 71 citations
  3. 3Less computational approach to detect QRS complexes in ECG rhythms2021
  4. 4Less computational approach to detect QRS complexes in ECG rhythms2020
  5. 5Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis2015 · 122 citations