77 lines
3.1 KiB
Markdown
77 lines
3.1 KiB
Markdown
# ECGSynKit
|
||
|
||
Swift Package for generating synthetic ECG signals. Includes implementation of ECGSYN [[1]](#1).
|
||
|
||
# Notes
|
||
|
||
Implementation roughly corresponds with MATLAB/Java implementation of ECGSYN [[2]](#2) with a few
|
||
implementation differences to improve performance. The library is also structured so that expensive
|
||
computations can be reused for multiple signals and a single RR series can be reused to generate multi-lead
|
||
ECGs.
|
||
|
||
This implementation generalizes the attractor array to arbitrary size to allow modeling R′ and S′ morphologies.
|
||
|
||
# Usage
|
||
|
||
Initialize `TimeParameters` and `RRParameters`:
|
||
```swift
|
||
import ECGSynKit
|
||
|
||
let timeParameters = TimeParameters(
|
||
numBeats: 500,
|
||
srInternal: 10, // internal generator sampling rate
|
||
decimateFactor: 2, // decimation factor (output signal sample rate will be `srInternal / decimateFactor`)
|
||
hrMean: 60, // mean heart rate in BPM
|
||
hrStd: 1, // heart rate standard deviation
|
||
seed: 1) // rng seed value
|
||
|
||
let rrParameters = RRParameters(
|
||
flo: 0.1, // low frequency oscillation (Mayer wave) in Hz
|
||
flostd: 0.01, // low frequency oscillation standard deviation
|
||
fhi: 0.25, // high frequency oscillation (respiration) in Hz
|
||
fhistd: 0.01, // high frequency oscillation standard deviation
|
||
lfhfratio: 0.5) // low frequency to high frequency power ratio
|
||
```
|
||
|
||
Initialize an RR Series generator. This will prepare an FFT setup for the required signal size.
|
||
Afterwards generate the RR Series:
|
||
|
||
``` swift
|
||
let rrGenerator = ECGSynRRGenerator(params: timeParameters)
|
||
let rrSeries = rrGenerator.generateSeries(params: rrParameters)
|
||
```
|
||
|
||
The `rrSeries` can be used multiple times such as to generate multilead ECG signals.
|
||
|
||
|
||
The `attractors` parameter specify the morphology of the exponential extrema of the ECG. For a normal
|
||
ECG there are 5 attractors corresponding to PQRST. The parameters `θ`, `a`, `b` are as described in the paper
|
||
and are angular position around the signal limit circle, amplitude, and width. The `θrf` parameter is optional
|
||
and is a generalization of the stretching factors for `θ`. Each `θ` will be adjusted by `θ * pow(hrMean / 60.0, θrf)`.
|
||
|
||
``` swift
|
||
let params = ECGSyn.Parameters(
|
||
range: (-0.4, 1.2) // the voltage range to scale the signal in mV
|
||
noiseAmplitude: 0.01, // the amplitude of the additive uniform noise
|
||
attractors: [
|
||
// θ, a, b, θrf
|
||
.make(deg: -70, 1.2, 0.25, 0.25),
|
||
.make(deg: -15, -5.0, 0.1, 0.5),
|
||
.make(deg: 0, 30, 0.1),
|
||
.make(deg: 15, -7.5, 0.1, 0.5),
|
||
.make(deg: 100, 0.75, 0.4, 0.25),
|
||
]
|
||
)
|
||
|
||
let signal = ECGSyn.generate(params: timeParameters) // signal is [Double]
|
||
```
|
||
|
||
|
||
# References
|
||
|
||
<a id="1">[1]</a>
|
||
McSharry PE, Clifford GD, Tarassenko L, Smith L. A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3): 289-294; March 2003.
|
||
|
||
<a id="2">[2]</a>
|
||
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
|