# Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia

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## Abstract

**:**

## 1. Introduction

#### 1.1. Related Work

#### 1.2. Proposed Work

## 2. Deep Learning Algorithms

#### 2.1. Fundamental Concepts of Convolutional Neural Networks

- The convolutional layer (CONV), which processes the received input data;
- The pooling layer (POOL), which allows compressing the information by reducing the size of the intermediate image (often by subsampling);
- The Fully Connected Layer (FCL) layer, which is a perceptron-type layer;
- The classification layer (Softmax), which predicts the class of the input image.

#### 2.1.1. Convolutional Layer

#### 2.1.2. Nonlinear Activation Function

#### 2.1.3. Pooling Layer

#### 2.1.4. Fully Connected Layer

#### 2.1.5. Loss Function

#### 2.2. Optimization of Hyperparameters

- Number of layers [42]: A conventional CNN typically consists of multiple layers, including convolutional layers, activation layers (e.g., ReLU), pooling layers, and fully connected layers.
- Filter size (Kernel Size) [43]: The size of the filters used in the convolutional layers is an important parameter. Common filter sizes are 3 × 3, 5 × 5, and 7 × 7.
- Number of filters [44]: The number of filters in each convolutional layer determines the depth of the feature maps generated. More filters lead to more expressive power but also increase computation requirements.
- Stride [45]: The stride determines the step size at which the filter is moved across the input image. Common values are 1 and 2, with larger strides reducing the size of the output feature maps.
- Padding [45]: Padding can be used to preserve the spatial dimensions of the input when convolving with filters. Common padding values are ’same’ and ’valid’.
- Activation function [46]: Common activation functions include ReLU (rectified linear unit), leaky ReLU, and Sigmoid. ReLU is widely used due to its simplicity and effectiveness.
- Pooling [47]: Pooling layers downsample the feature maps reduces the spatial dimensions. Common pooling types are Max pooling and average pooling, typically with a pool size of $2\times 2$.
- Fully connected layers [48]: The number of neurons in the fully connected layers can vary based on the complexity of the task. The output layer size depends on the number of classes in the classification task.
- Dropout [49]: Dropout is a regularization technique that randomly sets a fraction of neurons to zero during training, preventing overfitting. Common dropout rates are between 0.2 and 0.5.
- Batch size [50]: The number of samples used in each iteration during training. Smaller batch sizes are computationally more expensive but can lead to better convergence.
- Number of epochs [51]: This is the number of times the entire training dataset is passed through the network during training.
- Learning rate [52]: The learning rate controls the step size during optimization. A small learning rate leads to slow convergence, while a large learning rate can cause instability.
- Optimizer: Common optimizers used in CNNs include Stochastic Gradient Descent (SGD) [53], Adam, and RMSprop.

#### 2.3. CNN Architectures

#### 2.3.1. AlexNet

#### 2.3.2. VGGNet

#### 2.3.3. Inception V3

#### 2.3.4. MobileNet

## 3. Time–Frequency Representation

## 4. Material and Methods

- First phase: The dataset used is described.
- Second phase: The ECG data undergoes filtering to reduce baseline interference. Once filtered, the Window Reference Mark (WRM) of the ECG signal is obtained. Each WRM indicates the start of a time window (tw) within the ECG signal.
- Third phase: Information extraction is performed by applying the Hilbert transform (Ht) to each window tw obtained in the first phase. Subsequently, the TFR matrix is computed using the Pseudo Wigner–Ville method, resulting in the Time–Frequency Representation Image (TFRI).
- Fourth phase: The TFRI matrices obtained in the previous step are used as input for a deep learning CNN (CNN1, CNN2, InceptionV3, MobilNet, VGGNet, and AlexNet), as detailed in Section 2.3 and Section 4.4.1. The success of ventricular fibrillation (VF) detection relies on signal processing techniques and the structure of the classifiers employed. To achieve optimal performance, it is necessary to adjust the CNN parameters to better adapt to the data.

#### 4.1. Materials

#### 4.2. Electrocardiographic Signal Preprocessing

#### 4.2.1. Denoising

#### 4.2.2. Segmentation

#### 4.3. Extraction of Image from TFR

#### 4.4. Model Training and Evaluation

#### 4.4.1. Model Architecture

- In the CNN1 method, 2 fully connected layers utilize the output from the TFR and predict the class of the image based on the vector calculated in previous stages.
- In the CNN2 method, the network consists of 6 layers, including 2 convolution layers, 2 max-pooling layers, and 2 fully connected layers. Each convolution layer (layers 1 and 2) applies convolution with its respective kernel size (layers 3 and 4). Following each convolution layer, a max-pooling operation is performed on the generated feature maps. The purpose of max-pooling is to reduce the dimensionality of the feature maps, aiding in the extraction of essential features.

#### 4.4.2. Training the Convolutional Neural Network Model

#### 4.5. Performance Metrics for Classification

## 5. Results

- In the TFR_CNN1 approach, we initially transformed each tw into a time–frequency Representation Image (TFRI) utilizing the Pseudo Wigner–Ville transform, without using the Hilbert transform (Ht). The resulting image was then converted into a feature vector, which served as input for the Fully Connected Layer (FCL) of the classifier.
- In the Ht_TFR_CNN1 method, information extraction involved applying the Hilbert transform to each window’s tw obtained in the first phase, followed by the assessment of the Time–Frequency Representation (TFR) matrix using the Pseudo Wigner–Ville transform. The resulting TFR matrix was used to generate the TFRI, which was then used as input for the FCL.
- In the Ht_TFR_CNN2 method, the parameters were extracted using CNN2 by combining the Hilbert transform (Ht) and the TFRI. The extracted vectors were then used as input for the FCL.

#### Analysis Based on Different CNN Algorithms

## 6. Discussion

## 7. Application in a Real Clinical Setting

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**Activation functions commonly applied to neural networks: (

**a**) rectified linear unit (ReLu), (

**b**) Sigmoid, and (

**c**) hyperbolic tangent (Tanh).

**Figure 5.**$PWV$ distribution of the ECG $Normal$ signal directly processed without the Hilbert transform. $PWV$ distribution of the $Normal$ analytic signal using the Hilbert transform.

**Figure 6.**A comprehensive diagram outlines the series of processing steps applied in the detection of ventricular fibrillation.

**Figure 7.**IIR bandpass filter applied to a Normal-type ECG. The original temporal signal is plotted in blue, and the filtered output signal is shown in red. The frequency response of the filter is displayed below.

**Figure 8.**In the illustration, the columns, from top to bottom, represent the original ECG time signal window, TFR ($150\times 150$), TFR + Ht ($150\times 150$), TFR + Ht ($45\times 150$), and TRFI ($45\times 150$), respectively. From left to right, they correspond to the classes $Normal$, $Other$, $VT$, and $VF$, respectively.

**Figure 9.**Loss function diagram. The figure shows the function image of the model training CNN2; the train loss is 0.02, and the val loss is 0.1.

**Figure 10.**Accuracy function. The figure shows the function image of model training Ht_TFR_CNN2; the train accuracy is 100%, and the val accuracy is 98%.

**Figure 11.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the TFR_CNN1 technique (Epochs = 50).

**Figure 12.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the Ht_TFR_CNN1 technique (Epochs = 50).

**Figure 13.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the Ht_TFR_CNN1 technique (Epochs = 100).

**Figure 14.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the Ht_TFR_CNN2 method (Epochs = 100).

**Figure 15.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the VGGNet method (Epochs = 6).

**Figure 16.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the Alexnet method (Epochs = 6).

**Figure 17.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the Mobilnet method (Epochs = 6).

**Figure 18.**Confusion matrix for classifying $Normal$, $Other$, $VT$, and $VF$ classes utilizing the InceptionV3 method (Epochs = 6).

Model | CNN1 | ||
---|---|---|---|

Layer | Kernel Size | Filter Number | #Parameters |

FC1 | 512 | - | 16589312 |

FC2 | 256 | - | 131328 |

Softmax | 4 | - | 1285 |

Model | CNN2 | ||

Layer | Kernel Size | Filter Number | #Parameters |

Conv1 | 3 × 3 | 32 | 320 |

Max Pooling1 | 4 × 4 | - | 0 |

Conv2 | 3 × 3 | 64 | 18496 |

Max Pooling2 | 4 × 4 | - | 0 |

FC1 | 128 | - | 991360 |

FC2 | 256 | - | 33024 |

Softmax | 4 | - | 1028 |

Class | Normal | ||||||
---|---|---|---|---|---|---|---|

Algorithms | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

Normal | Global | VF | VT | Other | Total | Total | |

Ht_TFR_CNN1 (Epochs = 50) | 89.70 | 98.57 | 99.53 | 99.48 | 97.73 | 98.76 | 93.92 |

Ht_TFR_CNN1 (Epochs = 100) | 99.29 | 98.62 | 98.88 | 99.33 | 98.03 | 98.91 | 98.95 |

Ht_TFR_CNN2 (Epochs = 100) | 99.34 | 98.35 | 99.59 | 99.83 | 99.59 | 98.89 | 98.84 |

TFR_CNN1 (Epochs = 50) | 98.70 | 98.59 | 99.46 | 98.73 | 97.73 | 98.65 | 98.64 |

Class | Other | ||||||
---|---|---|---|---|---|---|---|

Algorithms | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

Other | Global | VT | Normal | VF | Total | Total | |

Ht_TFR_CNN1 (Epochs = 50) | 97.24 | 99.41 | 99.82 | 99.29 | 99.65 | 98.95 | 98.31 |

Ht_TFR_CNN1 (Epochs = 100) | 97.74 | 99.62 | 99.83 | 99.60 | 99.58 | 99.22 | 98.67 |

Ht_TFR_CNN2 (Epochs = 100) | 96.98 | 99.68 | 99.96 | 99.61 | 99.79 | 99.11 | 98.31 |

TFR_CNN1 (Epochs = 50) | 97.24 | 99.47 | 100 | 99.33 | 99.73 | 98.98 | 98.34 |

Class | VT | ||||||
---|---|---|---|---|---|---|---|

Algorithms | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

VT | Global | VF | Other | Normal | Total | Total | |

Ht_TFR_CNN1 (Epochs = 50) | 89.70 | 99.70 | 96.71 | 99.84 | 99.94 | 99.00 | 94.43 |

Ht_TFR_CNN1 (Epochs = 100) | 92.70 | 99.53 | 97.78 | 99.94 | 99.92 | 99.06 | 95.99 |

Ht_TFR_CNN2 (Epochs = 100) | 90.45 | 99.73 | 96.92 | 99.94 | 99.98 | 99.09 | 94.86 |

TFR_CNN1 (Epochs = 50) | 95.84 | 97.19 | 98.55 | 99.84 | 99.84 | 97.90 | 96.51 |

Class | VF | ||||||
---|---|---|---|---|---|---|---|

Algorithms | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

VF | Global | VT | Other | Normal | Total | Total | |

Ht_TFR_CNN1 (Epochs = 50) | 98.04 | 98.94 | 90.96 | 99.64 | 99.68 | 98.77 | 98.48 |

Ht_TFR_CNN1 (Epochs = 100) | 96.44 | 99.28 | 94.01 | 99.74 | 99.76 | 98.75 | 97.83 |

Ht_TFR_CNN2 (Epochs = 100) | 98.16 | 99.07 | 91.56 | 99.74 | 99.83 | 98.91 | 98.61 |

TFR_CNN1 (Epochs = 50) | 85.88 | 99.30 | 96.58 | 99.64 | 99.52 | 96.82 | 92.10 |

Class | Normal | ||||||
---|---|---|---|---|---|---|---|

Techniques | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

Normal | Global | VF | VT | Other | Total | Total | |

Ht_TFR_CNN1 (Epochs = 100) | 99.29 | 98.62 | 98.88 | 99.33 | 98.03 | 98.91 | 98.95 |

Ht_TFR_CNN2 (Epochs = 100) | 99.34 | 98.35 | 99.59 | 99.83 | 99.59 | 98.89 | 98.84 |

InceptionV3 (Epochs = 6) | 77.99 | 99.65 | 99.92 | 39.30 | 99.32 | 87.17 | 87.49 |

MobilNet (Epochs = 6) | 79.42 | 99.44 | 99.08 | 99.36 | 99.64 | 88.39 | 88.30 |

VGGnet (Epochs = 6) | 96.61 | 98.32 | 97.97 | 100 | 98.59 | 97.39 | 97.45 |

AlexNet (Epochs = 6) | 99.43 | 97.29 | 98.69 | 100 | 95.83 | 98.45 | 98.34 |

Class | Other | ||||||
---|---|---|---|---|---|---|---|

Techniques | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

Other | Global | VT | Normal | VF | Total | Total | |

Ht_TFR_CNN1 (Epochs = 100) | 97.74 | 99.62 | 99.83 | 99.60 | 99.58 | 99.22 | 98.67 |

Ht_TFR_CNN2 (Epochs = 100) | 96.98 | 99.68 | 99.96 | 99.61 | 99.79 | 99.11 | 98.31 |

InceptionV3 (Epochs = 6) | 88.42 | 99.81 | 100 | 99.72 | 100 | 96.96 | 93.77 |

MobilNet (Epochs = 6) | 99.64 | 85.08 | 98.41 | 79.60 | 97.68 | 88.21 | 91.78 |

VGGnet (Epochs = 6) | 98.54 | 97.57 | 100 | 96.74 | 99.26 | 97.39 | 98.05 |

AlexNet (Epochs = 6) | 95.78 | 99.57 | 100 | 99.58 | 99.40 | 98.77 | 97.63 |

Class | VT | ||||||
---|---|---|---|---|---|---|---|

Techniques | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

VT | Global | VF | Other | Normal | Total | Total | |

Ht_TFR_CNN1 (Epochs = 100) | 92.70 | 99.53 | 97.78 | 99.94 | 99.92 | 99.06 | 95.99 |

HT_TFR_CNN2 (Epochs = 100) | 90.45 | 99.73 | 96.92 | 99.94 | 99.98 | 99.09 | 94.86 |

InceptionV3 (Epochs = 6) | 98.15 | 83.55 | 99.11 | 99.04 | 80.18 | 84.59 | 90.26 |

MobilNet (Epochs = 6) | 95.53 | 97.66 | 98.89 | 100 | 99.90 | 97.49 | 96.58 |

VGGnet (Epochs = 6) | 90.15 | 99.15 | 97.07 | 99.94 | 100 | 98.77 | 94.43 |

AlexNet (Epochs = 6) | 91.84 | 99.47 | 97.54 | 99.94 | 100 | 98.94 | 95.50 |

Class | VF | ||||||
---|---|---|---|---|---|---|---|

Techniques | Sensitivity (%) | Specificity (%) | Accuracy (%) | F Score (%) | |||

VF | Global | VT | Other | Normal | Total | Total | |

Ht_TFR_CNN1 (Epochs = 100) | 96.44 | 99.28 | 94.01 | 99.74 | 99.76 | 98.75 | 97.83 |

Ht_TFR_CNN2 (Epochs = 100) | 98.16 | 99.07 | 91.56 | 99.74 | 99.83 | 98.91 | 98.61 |

InceptionV3 (Epochs = 6) | 77.28 | 94.90 | 98.15 | 89.72 | 96.86 | 91.28 | 85.18 |

MobilNet (Epochs = 6) | 86.97 | 99.62 | 97.33 | 100 | 99.80 | 97.01 | 92.86 |

VGGnet (Epochs = 6) | 93.34 | 99.25 | 92.28 | 100 | 99.85 | 98.14 | 96.20 |

AlexNet (Epochs = 6) | 95.58 | 99.34 | 93.42 | 100 | 99.84 | 98.64 | 97.42 |

**Table 10.**Comparison of proposed CNN architecture for applications in detecting $Normal$, $Other$, $VT$, and $VF$ classes with other techniques.

Class | VF | VT | Other | Normal | Data Base | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Techniques | Sens (%) | Spe (%) | Acc (%) | Sens (%) | Spe (%) | Acc (%) | Sens (%) | Spe (%) | Acc (%) | Sens (%) | Spe (%) | Acc (%) | |

This work, Ht_TFR_CNN1 (Epochs = 50) | 98.04 | 98.94 | 98.77 | 89.7 | 99.70 | 99 | 97.24 | 99.41 | 98.95 | 89.7 | 98.57 | 98.76 | MITBIH, AHA |

This work, Ht_TFR_CNN1 (Epochs = 100) | 96.44 | 99.28 | 98.75 | 92.70 | 99.53 | 99.06 | 97.74 | 99.62 | 99.22 | 99.29 | 98.62 | 98.91 | MITBIH, AHA |

This work, Ht_TFR_CNN2 (Epochs = 100) | 98.16 | 99.07 | 98.91 | 90.45 | 99.73 | 99.09 | 96.98 | 99.68 | 99.11 | 99.34 | 98.35 | 98.89 | MITBIH, AHA |

This work, InceptionV3 (Epochs = 6) | 77.28 | 94.9 | 91.28 | 98.15 | 83.55 | 84.59 | 88.42 | 99.81 | 96.96 | 77.99 | 99.65 | 87.17 | MITBIH, AHA |

This work, MobilNet (Epochs = 6) | 86.97 | 99.62 | 97.01 | 95.53 | 97.66 | 97.49 | 99.64 | 85.08 | 88.21 | 79.42 | 99.44 | 88.39 | MITBIH, AHA |

This work, VGGnet (Epochs = 6) | 93.34 | 99.25 | 98.14 | 90.15 | 99.15 | 98.77 | 98.54 | 97.57 | 97.39 | 96.61 | 98.32 | 97.39 | MITBIH, AHA |

This work, AlexNet (Epochs = 6) | 95.58 | 99.34 | 98.64 | 91.84 | 99.47 | 98.94 | 95.78 | 99.57 | 98.77 | 99.43 | 97.29 | 98.45 | MITBIH, AHA |

[58] SSVR, TFR | 91 | 97 | 92.8 | 98.7 | 92.3 | 99.2 | 96.6 | 96.3 | MITBIH, AHA | ||||

[58] BAGG, TFR | 95.2 | 96.4 | 88.8 | 99.7 | 88.6 | 99.8 | 96.6 | 94.1 | MITBIH, AHA | ||||

[58] I2-RLR and TFR | 89.6 | 96.7 | 91 | 98.1 | 92.5 | 98.1 | 94.9 | 96.4 | MITBIH, AHA | ||||

[58] ANNC and TFR | 92.8 | 97 | 91.8 | 98.7 | 92.9 | 99 | 96.2 | 96.7 | MITBIH, AHA | ||||

[66] TCSC algorithm | 80.97 | 98.51 | 98.14 | MITBIH, CUDB | |||||||||

[67] Chaotic based | 88.6 | MITBIH, CCU | |||||||||||

[68] SVM and FS | 91.9 | 97.1 | 96.8 | MITBIH, CUDB | |||||||||

[69] SVM and Genetic algorithm | 98.4 | 98 | 96.3 | CUDB, AHA | |||||||||

[70] SVM and EMD | 99.99 | 98.4 | 99.19 | MITBIH, CUDB | |||||||||

[71] CNN neural network | 56.44 | 98.19 | 97.88 | MITBIH, CUDB | |||||||||

[72] EMD and Lempel-Ziv | 98.15 | 96.01 | 96.01 | 98.15 | MITBIH, CUDB | ||||||||

[73] TDA | 97.07 | 99.25 | 98.68 | 92.72 | 99.53 | 99.05 | 97.43 | 99.54 | 99.09 | 99.05 | 98.45 | 98.76 | MITBIH, AHA |

[73] PDI | 84.34 | 96.77 | 94.26 | 82.25 | 98.53 | 97.38 | 92.86 | 97.15 | 96.19 | 93.09 | 92.14 | 92.65 | MITBIH, AHA |

[74] App Entropy and EMD | 90.47 | 91.66 | 91.17 | 90.62 | 91.11 | 90.8 | MITBIH | ||||||

[75] Approximated entropy | 97.98 | 97.03 | 97.03 | 97.98 | MITBIH, CUDB |

**Table 11.**Comparison of proposed CNN architecture for applications in detecting ventricular fibrillation and tachycardia with other techniques.

Class | Shockable (VT+VF) | Data Base | ||
---|---|---|---|---|

Technique | Sensitivity (%) | Specificity (%) | Accuracy (%) | |

This work, Ht_TFR_CNN1 | 98.53 | 99.69 | 99.39 | MITBIH, AHA |

This work, Ht_TFR_CNN2 | 99.23 | 99.74 | 99.61 | MITBIH, AHA |

[73] TDA | 99.03 | 99.67 | 99.51 | MITBIH, AHA |

[73] PDI | 89.63 | 96.96 | 95.12 | MITBIH, AHA |

[76] CNN | 95.32 | 91.04 | 93.2 | MITDB, CUDB, VFDB |

[14] VMD with Random Forest | 96.54 | 97.97 | 97.23 | MITBIH, CUDB |

[77] RNN | 99.72 | MITBIH | ||

[78] CNN and IENN | 98.6 | 98.9 | 98.8 | MITBIH, AFDB |

[68] FS and SVM | 95 | 99 | 98.6 | MITBIH, CUDB |

[79] Personalized features SVM | 95.6 | 95.5 | MITBIH, CUDB, VFDB | |

[16] C4.5 classifier | 90.97 | 97.86 | 97.02 | MITBIH, CUDB |

[69] SVM and bootstrap | 98.4 | 98 | 98.1 | MITBIH, AHA, CUDB |

[80] Adaptive variational and boosted CART | 97.32 | 98.95 | 98.29 | MITBIH, CUDB |

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## Share and Cite

**MDPI and ACS Style**

Mjahad, A.; Saban, M.; Azarmdel, H.; Rosado-Muñoz, A.
Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia. *J. Imaging* **2023**, *9*, 190.
https://doi.org/10.3390/jimaging9090190

**AMA Style**

Mjahad A, Saban M, Azarmdel H, Rosado-Muñoz A.
Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia. *Journal of Imaging*. 2023; 9(9):190.
https://doi.org/10.3390/jimaging9090190

**Chicago/Turabian Style**

Mjahad, Azeddine, Mohamed Saban, Hossein Azarmdel, and Alfredo Rosado-Muñoz.
2023. "Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia" *Journal of Imaging* 9, no. 9: 190.
https://doi.org/10.3390/jimaging9090190