###### Abstract  
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection have been developed in previous research. However, the existing research landscape requires innovative approaches that can further improve the accuracy of diagnosing and managing patients. This study investigates the application of variable-frequency complex demodulation (VFCDM) and convolutional neural networks (CNN) to discriminate between healthy, interictal, and ictal states using electroencephalogram (EEG) data. For testing this approach, the EEG signals were collected from the publicly available Bonn dataset. A high-resolution time-frequency spectrum (TFS) of each EEG signal was obtained using the VFCDM. The TFS images were fed to the CNN classifier for the classification of the signals. The performance of CNN was evaluated using leave-one-subject-out cross-validation (LOSO CV). The TFS shows variations in its frequency for different states that correspond to variation in the neural activity. The LOSO CV approach yields a consistently high performance, ranging from 90% to 99% between different combinations of healthy and epilepsy states (interictal and ictal). The extensive LOSO CV validation approach ensures the reliability and robustness of the proposed method. As a result, the research contributes to advancing the field of epilepsy detection and brings us one step closer to developing practical, reliable, and efficient diagnostic tools for clinical applications. epilepsy; time-frequency analysis; variable-frequency complex demodulation; convolutional neural networks +
Footnote †: journal: Accepted: 25 November 2023  
signals for any morphologic abnormalities representative of this activity that can indicate the presence of or even the type of epilepsy a patient may have to arrive at a final diagnosis. While accuracy rates of epileptic diagnosis are high, traditional screening for epilepsy, most specifically the manual interpretation of EEG signals, has proven to be time-consuming when provided with many hours of recordings [9]. It has also shown to be variable and inaccurate in several cases, as the EEG readers level and technique of analysis may vary [10]. While this problem has been alleviated with the introduction of automatic seizure detection software, such implementations generally have poor sensitivity and are overfitted to a specific seizure occurrence, thus rendering them uncommon in most clinical settings. To avoid such issues and improve accuracy, new research has proposed various improvements and novel approaches to epileptic computer-aided diagnosis. Recent research includes, but is not limited to, non-linear-, frequency-, time-frequency-, and time-based methods, as discussed by [PERSON] et al. [11]. Most of these methods have followed a similar approach, first establishing and extracting specific epileptic features in EEG waveforms, then performing a statistical analysis to rank the features, and finally, identifying the ideal classifier based on their respective performances. To further improve these methods, some groups have implemented deep convolution neural networks (CNN) to avoid feature extraction and selection [12; 13; 14; 15]. This work has used custom CNNs and various DL models (Alexnet, VGG16, ResNet, etc.) in both 1D [12] and 2D [14; 15] domains. The 2D approaches have explored the use of various inputs, including scaled spectrograms, the delta, and continuous wavelet transform scalograms. In contrast to traditional machine learning algorithms, such as support vector machines or random forests, CNNs excel at automatically extracting hierarchical features from data, particularly in image-based tasks [16; 17].