Signal Segmentation

In EGM: Evaluating Cardiac Electrophysiology Signals

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(EGM)

The concept of beat segmentation is important in cardiac electrical signal analysis. There are many approaches that can be used, based on the underlying rhythm. The simplest is to use a sinus rhythm as the baseline, while more complex would be rapid macro-reentry.

Windowing or segmenting signals helps with identify characteristics of individual beats or events. These can subsequently be leveraged in many ways, such as...

• Machine learning approaches on single beat data
• Signal averaging to create template beats
• Visualizing windowed beats

Sinus rhythm

The initial approach will be to use sinus rhythm, which can most easily be evaluated using a rule-based approach:

1. Between an $QRS_{i}$ (index QRS complex) and $QRS_{i+1}$ (following QRS complex), there must be a T wave
2. Between the $QRS_{i}$ and the $QRS_{i-1}$ (previous QRS complex), there must be P wave ≥ 1
3. There should not be additional depolarization signals between the $P_{i}$ and $QRS_{i}$

#| eval: false
ecg <- read_wfdb(record = 'muse-sinus',
                 record_dir = system.file('extdata', package = 'egm'),
                 annotator = 'ecgpuwave')
# Example data
ecg

This file represent an ECG data set obtained from MUSE v9 that contains 12-leads of data over 10 seconds.

EGM documentation built on June 22, 2024, 6:53 p.m.