# EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Database

#### 2.2. Variable-Frequency Complex Demodulation Algorithm

#### 2.2.1. Complex Demodulation

#### 2.2.2. VFCDM

- 1.
- Filter Design: Design a finite-impulse response LPF with a specified bandwidth (${F}_{w})$ and filter length (${N}_{w})$. Set center frequencies as (${f}_{0i})$:$${f}_{0i}=2\times {F}_{w}\left(i-1\right),i=\mathrm{1,2},3,\dots ,int\left[\raisebox{1ex}{${f}_{max}$}\!\left/ \!\raisebox{-1ex}{$2\times {F}_{w}$}\right.\right]$$
- $2\times {F}_{w}$ = Spacing between neighboring center frequencies,
- ${f}_{max}$ = Highest signal frequency.

- 2.
- CDM Dominant Frequency Extraction: Employ the CDM technique to identify the dominant frequency within the defined bandwidth and iterate this process by incrementing ${f}_{0i}$ across the entire frequency band.
- 3.
- Signal Decomposition: Decompose the signal into sinusoidal modulations using the CDM.
- 4.
- Instantaneous Frequency Calculation: Calculate the instantaneous frequencies using Equation (9), based on the phase (as per Equation (5)) and the instantaneous amplitudes (as per Equation (4)) of each sinusoidal modulation component, using the Hilbert transform.
- 5.
- Time–Frequency Representation: Obtain the TFR of the signal by using the estimated instantaneous frequencies and amplitudes, providing a detailed depiction of signal variations across both time and frequency domains. For more details about the VFCDM algorithm, please refer to [21].

#### 2.3. Convolutional Neural Networks

- (1)
- Convolution Layer (CL)

- (2)
- Pooling Layer (PL)

- (3)
- Fully Connected Layer (FC)

#### 2.4. Performance Evaluation of CNN

## 3. Results

^{4}dB. Both Z and O have a peak frequency around 3 Hz, indicating the frequency with the maximum power. The mean of dominant frequency for healthy states is about 5.4 Hz, which is relatively low. These states also exhibit 28% of significant frequency transitions, implying a moderate variability in neural dynamics.

^{4}dB, indicating less spectral power. Surprisingly, the peak frequency for interictal states reaches 500 Hz, suggesting a shift toward higher frequencies. A distinctive feature of the interictal state is the mean of dominant frequency, which is approximately 97 Hz, indicating increased activity in higher frequency bands. These states also have a moderate 5% of significant frequency transitions.

^{3}Hz, pointing to intense neural activity during seizures. The mean of maximum TFS reaches around 6.3788 × 10

^{4}dB, signifying high levels of neural activity during ictal events. However, this heightened activity also results in a significantly elevated stability of TFS. In terms of mean total power, the state exhibits notably high values, reaching approximately 6.8699 × 10

^{6}dB, indicating a substantial amount of spectral power in these signals. Unlike healthy and interictal states, the ictal state exhibits a peak frequency close to 0 Hz, which suggests a wide range of frequency activity during seizures. The mean dominant frequency is remarkably low, approximately 0.0072 Hz, underlining the diversity of neural dynamics during seizures. In contrast to the other states, ictal states have a low significant frequency transition, around 0.0244%, suggesting relatively stable frequency patterns during seizures.

#### 3.1. Case I: Healthy vs. Ictal State

#### 3.2. Case II: Healthy vs. Epilepsy Subjects

#### 3.3. Case III: Healthy vs. Interictal vs. Ictal States

## 4. Discussion

**Table 4.**Comparison of various studies related to the automatic detection of epileptic classes using the Bonn University database of EEG signals.

Author | Method | Classifier | Cross-Validation | Performance (%) | |||
---|---|---|---|---|---|---|---|

Acc | Pre | Rec | F1 | ||||

Faust et al. 2010 [38] | Yule–Walker | SVM ^{$} | ˗ | 93.3 | ˗ | ˗ | ˗ |

Acharya et al. 2009 [39] | Non-linear measures | GMM ^{$} | ˗ | 96.1 | ˗ | ˗ | ˗ |

Acharya et al. 2013 [40] | DWT Frequency Bands | SVM ^{$} | ˗ | 96.0 | ˗ | ˗ | ˗ |

Sharma et al. 2017 [47] | Flexible WT/Fractal Dimension | SVM | 10-Fold | 99.2 | ˗ | ˗ | ˗ |

Acharya et al. 2018 [13] | 1D EEG Features | CNN | 10-Fold | 88.7 | ˗ | ˗ | ˗ |

Ilias et al. 2023 [14] | Spectrogram/Delta | CNN | 10-Fold | 97.0 | 97.14–97.18 * | 96.00–97.99 * | 96.41–97.52 * |

Mahfuz et al. 2021 [15] | CWT | CNN | Split 10/90 | 98.46 | ˗ | ˗ | ˗ |

Hussein et al. 2019 [43] | TD | RNN/LSTM | 3/5/10-Fold | 100 | ˗ | ˗ | ˗ |

Chanu et al. 2023 [44] | DWT | SONN | Split 30/70 | 99.2 | 98 | 100 | 98.99 |

Islam et al. 2022 [45] | TD | Epileptic-Net | 10-Fold | 99.95–99.98 * | ˗ | ˗ | ˗ |

Yuan et al. 2017 [42] | CWT Scalogram | GPCA/SDAE | Split 80/20 | 100 | 100 | 100 | 100 |

Ullah et al. 2018 [12] | TD | P-1D-CNN | 10-Fold | 97.4–100 * | ˗ | ˗ | ˗ |

Guo et al. 2011 [49] | Genetic Programming | KNN | Split 60/40 | 93.5 | ˗ | ˗ | ˗ |

Bhattacharyya et al. 2017 [46] | EWT | RF | 10-Fold | 99.4 | ˗ | ˗ | ˗ |

Sharmila et al. 2018 [41] | DWT Entropy | SVM | Split 50/50 | 78–100 * | ˗ | ˗ | ˗ |

Goel et al. 2023 [48] | Recurrence Plots | SVM | - | 98.21 | 99.61 | ˗ | ˗ |

Abdulhay et al. 2020 [50] | Entropy, non-linear, and spectra | SCANN | 10-Fold | 98.5 | ˗ | ˗ | ˗ |

Proposed Approach | VFCDM | CNN | LOSO CV | 90–99 * | 96–99 * | 94–99 * | 93–99 * |

^{$}Performance of the classifier is evaluated using receiver operating characteristic (ROC) analysis. * Performance range in classifying between various combinations of healthy and epileptic states.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**EEG signal characteristics in different brain states. Representative EEG signals depicting neural activity in healthy, interictal, and ictal states. The figure illustrates distinctive amplitude and fluctuation patterns: Healthy state (Z and O): low and stable RMS amplitude (40–43 µV) with consistent fluctuations (28–29 µV). Interictal state (F and N): higher RMS amplitude (49–50.5 µV) with pronounced and erratic fluctuations. Ictal state (S): intermediate RMS amplitude (36.5 µV) with intense, synchronous neuronal activity.

**Figure 4.**Time–frequency spectrograms (TFS) of EEG signals across brain states. Representative TFS depicting neural activity in healthy (Z and O), interictal (F and N), and ictal (S) states. The figure showcases key TFS features, including the mean frequency range, spectral power, stability, dominant frequency, and % of significant frequency transitions.

**Figure 5.**Radar plot representation of the CNN performance metrics for classifying various combinations of healthy and ictal states.

**Figure 6.**The (

**a**) training and validation metrics and (

**b**) normalized confusion matrix of CNN in classifying various combinations of healthy and ictal states.

**Figure 7.**Radar plot representation of the CNN performance metrics for classifying healthy vs. epileptic subjects.

**Figure 8.**The (

**a**) training and validation metrics and (

**b**) normalized confusion matrix of CNN in classifying various combinations of healthy vs. epileptic subjects.

**Figure 9.**Radar plot representation of the CNN performance metrics for classifying healthy vs. interictal vs. ictal states.

**Figure 10.**The (

**a**) training and validation metrics and (

**b**) normalized confusion matrix of CNN in classifying healthy vs. interictal vs. ictal states.

**Table 1.**Detailed description of the database used in the study [22].

Healthy Subject | Epilepsy Subject | |||
---|---|---|---|---|

Eyes open | Eyes closed | Seizure-free interval (interictal state) | Seizure activity (ictal state) | |

Hippocampal formation | Hippocampal formation of the opposite hemisphere of the brain | |||

Z | O | N | F | S |

Case | Classes | Description |
---|---|---|

I | - Z vs. S
- O vs. S
- (Z+O) vs. S
| Healthy vs. Seizure |

II | - (Z+O) vs. (N+F) vs. (S)
| Healthy vs. Interictal vs. Ictal |

III | - (Z+O vs. N+F+S)
- (Z+O vs. N+F)
| Healthy vs. Epileptic |

Metric | Description | Expression |
---|---|---|

Acc | Measures the proportion of predictions that are correct. | $\frac{{T}_{P}+{T}_{N}}{{T}_{P}+{T}_{N}+{F}_{P}+{F}_{N}}$ |

Pre | Quantifies the accuracy of positive predictions. | $\frac{{T}_{P}}{{T}_{P}+{F}_{P}}$ |

Rec | Evaluates the model’s ability to identify all actual positives. | $\frac{{T}_{P}}{{T}_{P}+{F}_{N}}$ |

F1 | Harmonic means of precision and recall. It provides a balance between precision and recall, considering both false positives and false negatives. | $\frac{2\times \mathrm{P}\mathrm{r}\mathrm{e}\times \mathrm{R}\mathrm{e}\mathrm{c}}{\mathrm{P}\mathrm{r}\mathrm{e}+\mathrm{R}\mathrm{e}\mathrm{c}}$ |

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**MDPI and ACS Style**

Veeranki, Y.R.; McNaboe, R.; Posada-Quintero, H.F.
EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks. *Signals* **2023**, *4*, 816-835.
https://doi.org/10.3390/signals4040045

**AMA Style**

Veeranki YR, McNaboe R, Posada-Quintero HF.
EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks. *Signals*. 2023; 4(4):816-835.
https://doi.org/10.3390/signals4040045

**Chicago/Turabian Style**

Veeranki, Yedukondala Rao, Riley McNaboe, and Hugo F. Posada-Quintero.
2023. "EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks" *Signals* 4, no. 4: 816-835.
https://doi.org/10.3390/signals4040045