# Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

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## Abstract

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## 1. Introduction

- A simplified mathematical model based on the sum of two Gaussians has been proposed, which does not require baseline adjustment [23], and it is straightforward compared to the current models.
- Most fitting or optimizing techniques are designed to calculate only one Gaussian function, which is not ideal for the proposed method. Therefore, to solve these problems along with the model, two-hybrid optimization methods have been proposed.
- The proposed model’s performance is evaluated using time domain, frequency domain, and time-frequency domain analysis.
- Among various applications, one of the most promising ones is the ECG generator for education and research purpose. A graphical user interface (GUI) has been developed to show the proposed system’s potential use. Besides, noisy ECG generation and data compression have also been presented.

## 2. Background Problems and Proposed Model

#### 2.1. Proposed Model

#### 2.2. Optimization Problem Formulation and Proposed Optimization Method

**a**) Multi-start and (

**b**) Global search. In summary, ApproxiGlo comprises the Approximation method and Global search, whereas ApproxiMul comprises the Approximation method and Multi-start; see Figure 2.

- ⮚
- Step 1.1 After Segmenting ECG, beat into $P,\text{}Q,\text{}R,\text{}S,\text{}T$ components. i.e., $ECG\in P,Q,R,S,T$, minimum and maximum values of ${\sigma}_{1}\text{}$ are calculated based on the number of samples. Assume the total time duration of an ECG component ${x}_{R}$ and the number of samples in this time duration is ${N}_{s}$, the ${\sigma}_{{1}_{min}}$ and ${\sigma}_{{1}_{max}}\text{}$ can be approximated by the following equations:$$\left(\begin{array}{c}{\sigma}_{{1}_{min}}=\frac{\raisebox{1ex}{${x}_{R}$}\!\left/ \!\raisebox{-1ex}{${N}_{s}$}\right.}{5}\\ {\sigma}_{{1}_{max}}=\frac{{x}_{R}}{3}\end{array}\right\}$$
- ⮚
- Step 1.2 Increment the values of ${\sigma}_{1}$ from $\text{}{\sigma}_{{1}_{min}}$ to ${\sigma}_{{1}_{max}}$ at an interval of 0.3 and run through all the different values. Then, construct a Gaussian filter with the Gaussian as follows:$${G}_{filt}={\mathrm{e}}^{-\left[\frac{{S}^{2}}{{\sigma}_{1}\left(i\right)}\right]}\text{}$$$$S=-\left(\lceil \frac{{N}_{s}}{2}\rceil +1\right):1:\u23a3\lceil \frac{{N}_{s}}{2}\rceil \u23a6$$

- ⮚
- Step 1.3 Store the corresponding RMSE and corresponding parameters.
- ⮚
- Step 1.4 Repeat step 1.2 and step 1.3 for all values of ${\sigma}_{1}$.
- ⮚
- Step 1.5 Select the parameters (${A}_{1},\text{}{\sigma}_{1},\text{}{t}_{1}$) for which RMSE is the lowest.
- ⮚
- Step 1.6 Finally, replicate the parameter of the second Gaussian using the calculated Gaussian parameters, i.e., ${A}_{2}={A}_{1},\text{}{\sigma}_{2}={\sigma}_{1},\text{}{t}_{2}={t}_{1}$.

## 3. Databases and Pre-Processing

#### 3.1. Data Collection

#### 3.1.1. Experimental Data Collection

#### 3.1.2. Data Collection from Online

#### 3.2. Denoising

#### 3.3. ECG Components Extraction

## 4. Results

#### 4.1. Performance in Time Domain

#### 4.2. Performance in Frequency Domain

#### 4.3. Performance in Time-Frequency Domain

## 5. Applications of the Proposed Model

#### 5.1. Synthetic ECG Generator

#### 5.2. ECG Compression

## 6. Discussions

- Another limitation of this study is the number of ECG beats used. Few ECG beats were taken into account for model fitting and optimization. However, this work aims to find out an optimization method for ECG model fitting and the possible use of this optimized model in simulating different cardiac dysrhythmias, for example, atrial premature beat, paced beat, etc.
- The automatic classification of different types of ECG beats can be possible to implement by our proposed method. In that case, model parameters can be treated as features. These features can be trained by fuzzy-hybrid neural networks [56], support vector machine (SVM) [57,58], light gradient boosting machine [59], and Bayes maximum-likelihood (ML) classifier [32]. In addition to that, prominent features can also be selected by some feature selection algorithms such as the mRMR method and the Jaya algorithm [60,61,62] to increase the classification accuracy.
- A dictionary can be built based on our model and represent and classify cardiac dysthymias (which is called matching pursuit) [63].
- The proposed model can be used in model-based signal denoising [39].

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Performance Evaluation Metrics

#### Appendix A.1.1. Time-Domain Metrics

#### Appendix A.1.2. Frequency-Domain Metrics

#### Appendix A.1.3. Time-Frequency Domain Measure

#### Appendix A.2. Coefficient to Fit the with Models

Coefficient | Global | Multi Start | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

P | Q | R | S | T | P | Q | R | S | T | |

X(1) | −35.820 | 17.820 | 0.691 | 40.690 | −0.224 | −0.313 | −4.680 | 1.057 | −0.500 | 0.345 |

X(2) | 80.355 | 31.446 | 15.401 | 19.430 | 248.020 | 282.660 | 87.180 | 30.640 | 11.120 | 177.252 |

X(3) | 65.943 | 20.005 | 14.110 | 12.140 | 46.880 | 43.672 | 19.990 | 14.110 | 18.060 | 92.944 |

X(4) | 35.734 | −7.800 | 1.057 | −1.100 | 0.345 | 0.373 | 4.726 | 0.690 | 0.228 | −0.223 |

X(5) | 80.360 | 31.445 | 30.644 | 19.380 | 177.260 | 264.160 | 88.000 | 15.400 | 1.000 | 248.027 |

X(6) | 65.782 | 19.957 | 14.110 | 12.190 | 92.949 | 50.571 | 20.580 | 14.110 | 5.676 | 46.880 |

X(7) | 0.106 | −0.070 | −0.273 | 0.018 | −0.001 | 0.011 | −0.040 | −0.270 | 0.017 | −0.001 |

Size(samples) | 300.000 | 88.000 | 48.000 | 77.000 | 429.000 | 300.000 | 88.000 | 48.000 | 77.000 | 429.000 |

Coefficient | Global | Multi Start | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

P | Q | R | S | T | P | Q | R | S | T | |

X(1) | 0.016 | −2.044 | 0.729 | −0.074 | 0.026 | 0.033 | −0.074 | 0.729 | −0.072 | −0.083 |

X(2) | 17.222 | 5.545 | 4.304 | 2.470 | 129.154 | 13.498 | 17.292 | 4.310 | 4.403 | 84.398 |

X(3) | 3.542 | 5.132 | 3.610 | 1.587 | 45.606 | 7.044 | 3.311 | 3.610 | 0.521 | 14.599 |

X(4) | 0.017 | 2.089 | 1.514 | −0.074 | −0.072 | 0.022 | −0.022 | 1.512 | −0.162 | −0.034 |

X(5) | 11.850 | 5.549 | 8.766 | 2.470 | 85.103 | 31.278 | 5.117 | 8.768 | 2.321 | 6.509 |

X(6) | 3.428 | 5.284 | 3.126 | 1.587 | 12.684 | 10.343 | 1.442 | 3.124 | 1.244 | 44.711 |

X(7) | −0.046 | −0.105 | −0.343 | −0.054 | −0.043 | −0.059 | −0.043 | −0.342 | −0.053 | −0.018 |

Size(samples) | 44.000 | 20.000 | 13.000 | 14.000 | 153.000 | 44.000 | 20.000 | 13.000 | 14.000 | 153.000 |

Coefficient | Global | Multi Start | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

P | Q | R | S | T | P | Q | R | S | T | |

X(1) | −25.17 | −1.417 | 1.831 | −0.031 | 13.230 | 0.419 | −2.782 | 2.037 | −0.081 | 0.140 |

X(2) | 14.950 | 27.038 | 41.721 | 13.774 | 18.728 | 16.873 | 33.428 | 45.414 | 9.594 | 8.616 |

X(3) | 3.134 | 14.110 | 27.310 | 7.210 | 16.199 | 4.196 | 12.516 | 27.310 | 7.210 | 16.170 |

X(4) | 25.684 | −1.417 | −0.358 | −0.031 | −13.10 | 0.891 | −2.222 | 0.549 | −0.067 | 0.077 |

X(5) | 14.910 | 27.038 | 1.000 | 13.774 | 18.762 | 12.133 | 17.133 | 14.904 | 19.700 | 30.850 |

X(6) | 3.215 | 14.110 | 1.782 | 7.210 | 16.119 | 2.199 | 14.110 | 13.717 | 7.210 | 16.210 |

X(7) | −0.118 | −0.354 | 0.203 | 0.046 | −0.057 | −0.117 | 0.528 | −0.051 | 0.084 | −0.065 |

Size(samples) | 22.000 | 48.000 | 92.000 | 25.000 | 55.000 | 22.000 | 48.000 | 92.000 | 25.000 | 55.000 |

Coefficient | Global | Multi Start | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

P | Q | R | S | T | P | Q | R | S | T | |

X(1) | −0.296 | −0.054 | 1.335 | −0.555 | −0.147 | 0.160 | −0.054 | 1.335 | 0.481 | −0.067 |

X(2) | 5.788 | 13.840 | 20.368 | 20.059 | 1.004 | 62.969 | 13.840 | 20.368 | 1.000 | 1.000 |

X(3) | 14.440 | 4.518 | 4.852 | 24.310 | 29.709 | 10.323 | 4.518 | 4.852 | 2.114 | 4.760 |

X(4) | −0.126 | −0.021 | 1.244 | −0.507 | 0.161 | 0.329 | −0.021 | 1.244 | 0.270 | 0.066 |

X(5) | 22.613 | 19.132 | 14.064 | 60.740 | 15.084 | 41.613 | 19.133 | 14.065 | 82.000 | 29.099 |

X(6) | 8.348 | 1.539 | 7.810 | 24.310 | 29.709 | 16.954 | 1.538 | 7.810 | 11.569 | 29.710 |

X(7) | 0.037 | −0.023 | −0.099 | 0.142 | −0.034 | −0.252 | −0.023 | −0.099 | −0.401 | −0.038 |

Size(samples) | 63.000 | 22.000 | 27.000 | 82.000 | 100.000 | 63.000 | 22.000 | 27.000 | 82.000 | 100.000 |

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**Figure 2.**Proposed hybrid optimization technique. (

**a**) Step 1: Approximation of the Gaussian parameters. In this step, the effect of adding multiple Gaussians is not taken into account. Step 2: These estimated parameters are regarded as initial parameters in the (

**b**) Multi-start and (

**c**) Global search optimization solver to determine the optimal Gaussian parameters.

**Figure 5.**Model fitting of different cardiac dysrhythmias which are morphologically different and have different pathophysiological conditions.

**Figure 7.**FFT of different types of ECG beats representing physiological or pathophysiological conditions.

**Figure 8.**PSD of different types of ECG beats representing physiological or pathophysiological conditions.

**Figure 9.**Absolute scalogram difference of different types of ECG beats representing physiological or pathophysiological conditions using ApproxiGlo and ApproxiMul methods.

**Figure 12.**ECG simulation by model for 10 s. For bradycardia with BPM (beat per minute) 50, sinus rhythm with BPM 72, tachycardia with BPM 120.

**Table 1.**Comparison between the proposed ApproxiGlo and ApproxiMul and the nonlinear fitting method described in [24] for normal ECGs.

Average Goodness of Fitting | Nonlinear Fitting Method (MIT-BIH Arrhythmia Database) [24] | Proposed Hybrid Method (MIT-BIH Arrhythmia Database) | Proposed Hybrid Method (UCDSA Database) | ||
---|---|---|---|---|---|

ApproxiGlo | ApproxiMul | ApproxiGlo | ApproxiMul | ||

MSE | 0.00779 | 0.00071 | 0.0001 | 0.0007 | 0.0003 |

NMSE | 0.172477 | 0.01724 | 0.0031 | 6.4025 × 10^{−6} | 2.1465 × 10^{−6} |

RMSE | 0.0882615 | 0.02657 | 0.0112 | 0.0267 | 0.0164 |

NRMSE | 0.029748 | 0.1313 | 0.0555 | 0.0089 | 0.0054 |

CORR | 0.9205 | 0.99016 | 0.9983 | 0.9969 | 0.9988 |

Normal(N) | Atrial Premature Beat (A) | ||||
---|---|---|---|---|---|

Average goodness of fitting | ApproxiGlo | ApproxiMul | Average goodness of fitting | ApproxiGlo | ApproxiMul |

MSE | 0.00071 | 0.0001 | MSE | 6.29 × 10^{−5} | 3.33 × 10^{−5} |

NMSE | 0.01724 | 0.0031 | NMSE | 0.00175483 | 0.00092763 |

RMSE | 0.02657 | 0.0112 | RMSE | 0.00793334 | 0.005768 |

NRMSE | 0.1313 | 0.0555 | NRMSE | 0.04189072 | 0.03045697 |

CORR | 0.99016 | 0.9983 | CORR | 0.9991193 | 0.99953455 |

**Table 3.**Comparison between ApproxiGlo and ApproxiMul for paced beat and premature ventricular contraction beat.

Paced Beat (PB) | Premature Ventricular Contraction (V) | ||||
---|---|---|---|---|---|

Average goodness of fitting | ApproxiGlo | ApproxiMul | Average goodness of fitting | ApproxiGlo | ApproxiMul |

MSE | 0.0141746 | 0.0011756 | MSE | 0.00028384 | 0.00075337 |

NMSE | 0.0096177 | 0.0007977 | NMSE | 0.00173033 | 0.00459269 |

RMSE | 0.1190572 | 0.0342873 | RMSE | 0.01684748 | 0.02744755 |

NRMSE | 0.0980699 | 0.0282432 | NRMSE | 0.04159728 | 0.0677694 |

CORR | 0.9951467 | 0.9995984 | CORR | 0.99912836 | 0.9976848 |

Method | Frequency (Hz) | BPM | CR |
---|---|---|---|

Turning Point (TP) [42] | 200 | 75 | 2:1 |

Peak-Picking (spline) with Entropy coding [43] | 500 | 75 | 10:1 |

DPCM-Linear Prediction Interpolation and Entropy Coding [44] | 500 | 75 | 7.8:1 |

DWT using variable length code [45] | 360 | 75 | 22.19:1 |

DWT using direct binary representation [45] | 360 | 75 | 23:1 |

Polynomial transform (PT) [21] | 500 | 75 | 12.6 (N = 100) |

A dynamical model [39] | 128 | 60 | >7:1 |

Proposed method | 1 k | 75 | 20:1 |

200 | 75 | 4:1 | |

500 | 75 | 10:1 | |

360 | 75 | 7.2:1 | |

128 | 60 | 3.2:1 | |

128 | 75 | 2.56:1 |

Reference/Studies | Number of Gaussians | Additional Information | Type of Algorithm to Fit the Model | Outcomes |
---|---|---|---|---|

Suppappola et al. [6] | Between 6 and 14 (for normal ECG) | Chip away decomposition (CHAD) algorithm. Variable number of Gaussians. Prone to noise. | Three optimization methods used: Nelder Mead simplex method, Newton-Raphson and steepest descent method. | NMSE $\le $ 10% |

Clifford et al. [39] | 6 | A 3-D model. Complicated model. Asymmetry of T-wave is not considered. Can be stuck in local minima due to the use of local optimizer i.e. lsqnonlin. | Nonlinear least-squares solver using lsqnonlin function. | Presented visually |

McSharry et al. [9] | 5 | 3-D model. Complicated model. Asymmetry of T-wave is not considered. | Experimental search | Presented visually |

Clifford [46] | Adaptive determination for $p\text{}=\text{}n+2m$ ($n$ = Symmetric turning point, $m\text{}=$ asymmetric turning point) | A dynamical model. Asymmetry of T-wave is considered. Can be stuck in local minima due to the use of local optimizer, i.e., lsqnonlin. | Nonlinear least-squares solver using lsqnonlin function | Presented visually |

Parvaneh and Pashna [47] | Manually or Automatically (up to 133) | Gaussian function. Variable number of Gaussians. Complexity increases as the number of Gaussians increases. | Zero-crossing and minimum bank method | The best area under absolute of local error = 233.2 |

Badilini et al. [50] | 6 | Gaussian Messa Function (GMF) (consists of 5 parameters). GMF has more parameters than the single Gaussian function. | Generalized orthogonal forward regression (GOFR) | (CORR = 0.96) QT |

Dubois et al. [51] | 6 | GMF and nonlinear probability estimators. GMF has more parameters than the single Gaussian function. | GOFR | Used for classification |

Roonizi and Ebadollah [28] | 5 | Gaussian Function Riding on the Polynomial Background. Complex realization. | Non-iterative approximation method | PRD < 0.4 |

Dubois et al. [12] | 6 (GMF) For T wave (BiGaussian) | GMF and BiGaussian. Non-uniform Gaussian waves for different ECG components. | Generalized Orthogonal Forward Regression (GOFR) | QT (CORR = 0.92) |

Elda et al. [32] | Three different polynomials (linear, quadratic, cubic polynomials) | Adaptive Multi-harmonic ECG Modeling and Interacting multiple model (IMM) with sequential Markov chain Monte Carlo (SMCMC) methods. Complex to realize. | Sequential Bayesian-based methods to effectively model and adaptively select parameters ECG modeling techniques using the interacting multiple model (IMM). | ECG-P Wave RMSE = 2.71$\times {10}^{-2}$ for SMCMC and 2.29 $\times {10}^{-2}$ for IMM |

Proposed | 7 | Sum of two Gaussian functions. Simple to realize, adaptive to baseline wander, and does not stuck in local minima. However, the number of parameters is higher than single Gaussian and Bi-Gaussians. | Hybrid optimization techniques namely ApproxiGlo and ApproxiMul. | For ApproxiGlo CORR = 0.99016 RMSE = 0.02657 For ApproxiMul CORR = 0.9983 RMSE =0.0112 |

Average Performance Parameters | Crauna’s Method [27] | Guo’s Method [52] | FAS Method [29] | ApproxiGlo Method | ApproxiMul Method |
---|---|---|---|---|---|

MSE | 0.04314 | 0.05660 | 0.09497 | 0.00074 | 7.56$\times {10}^{-5}$ |

NMSE | 6.71 $\times {10}^{-5}$ | 8.81 $\times {10}^{-5}$ | 0.00015 | 1.17 $\times {10}^{-6}$ | 1.19$\times {10}^{-7}$ |

RMSE | 0.20767 | 0.23791 | 0.30817 | 0.02726 | 0.00869 |

NRMSE | 0.13454 | 0.15414 | 0.19965 | 0.01766 | 0.00563 |

CORR | 0.76189 | 0.73095 | 0.58196 | 0.99082 | 0.99907 |

Runtime (in Sec.) | 0.51107 | 0.00383 | 0.0034 | 4.51996 | 7.54061 |

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**MDPI and ACS Style**

Awal, M.A.; Mostafa, S.S.; Ahmad, M.; Alahe, M.A.; Rashid, M.A.; Kouzani, A.Z.; Mahmud, M.A.P.
Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias. *Sensors* **2021**, *21*, 1638.
https://doi.org/10.3390/s21051638

**AMA Style**

Awal MA, Mostafa SS, Ahmad M, Alahe MA, Rashid MA, Kouzani AZ, Mahmud MAP.
Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias. *Sensors*. 2021; 21(5):1638.
https://doi.org/10.3390/s21051638

**Chicago/Turabian Style**

Awal, Md. Abdul, Sheikh Shanawaz Mostafa, Mohiuddin Ahmad, Mohammad Ashik Alahe, Mohd Abdur Rashid, Abbas Z. Kouzani, and M. A. Parvez Mahmud.
2021. "Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias" *Sensors* 21, no. 5: 1638.
https://doi.org/10.3390/s21051638