Deep Learning Based Approach for Epilepsy Detection Using EEG Data / (Record no. 612592)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02015nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Khalid, Mina |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Learning Based Approach for Epilepsy Detection Using EEG Data / |
| Statement of responsibility, etc. | Mina Khalid |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 68p. |
| Other physical details | Islamabad : SMME- NUST; Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Epilepsy is one of the most common neurological disorders characterized by recurrent<br/>seizures. Electroencephalography (EEG) serves as a crucial diagnostic tool for epilepsy, yet<br/>traditional diagnosis relies heavily on manual interpretation, which is time-intensive, subjective,<br/>and prone to errors. This study addresses the need for automated, reliable, and efficient<br/>diagnostic methods by exploring the classification of healthy and epileptic individuals using raw<br/>EEG data analyzed through a one-dimensional Convolutional Neural Network (1D CNN). The<br/>proposed model was trained and evaluated on a dataset comprising 148 scalp EEG recordings<br/>(72 epileptic patients and 78 healthy individuals) obtained from a local hospital. The CNN model<br/>automatically extracted features from the EEG signals and achieved an accuracy of 97.73%,<br/>sensitivity of 98%, and specificity of 98%. Channel-specific analyses were conducted to evaluate<br/>the contribution of individual EEG channels, and the model's performance was further examined<br/>by progressively reducing the number of channels. These findings underscore the potential of<br/>integrating EEG data with deep learning for accurate, automated, and non-invasive epilepsy<br/>diagnosis. Additionally, the study highlights the significance of channel reduction in simplifying<br/>data while preserving diagnostic precision, facilitating more efficient clinical applications and<br/>real-time seizure detection systems. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Nabeel Anwar |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/48860">http://10.250.8.41:8080/xmlui/handle/123456789/48860</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 01/10/2025 | 610 | SMME-TH-1109 | Thesis |