# Signal Segmentation

    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 \(QRS_{i}\)

    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.