Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning / (Record no. 610840)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02238nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nayyab, Rida |
| 245 ## - TITLE STATEMENT | |
| Title | Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning / |
| Statement of responsibility, etc. | Rida Nayyab |
| 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 | 81p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Cardiovascular diseases are considered the major cause of death worldwide surpassing<br/>cancer. However, despite the broad category of diseases, research has been limited to<br/>binary classification i.e. normal and abnormal class leaving behind the accurate<br/>classification of specific diseases that affect the ECG waveform. PTB – XL database<br/>offers a wide variety of ECG records, but little research is dedicated to extracting<br/>morphological features for multi-class classification. Therefore, the paper used the open<br/>database to filter the ECG signal records having single unique labels and pre-processed<br/>them using the Butterworth bandpass filter and DWT db8. The Bandpass filter corrected<br/>baseline wander and reduced noise however, a high signal-to-noise ratio was achieved<br/>after applying 8-level DWT. The processed signals were fed into the Pan-Tompkins<br/>algorithm to extract R peaks. These peaks served as a baseline to identify other<br/>morphological features i.e. P-QRS-T intervals and amplitudes. These extracted features<br/>were labelled into 1 normal and 4 abnormal classes. There was a class imbalance in the<br/>dataset that could cause bias while training models. Therefore, SMOTE-NC was applied<br/>to upsample the dataset. The new dataset was split into the training set and the testing set.<br/>These sets were given as inputs to CNN and DNN models for a 5-fold loop. The<br/>performance was evaluated for both models using metrics like F1 score, recall, precision<br/>and accuracy. The CNN model achieved a mean accuracy of 81% whereas the mean<br/>accuracy for DNN was 84%. It was also noted that among the 5 classes, HYP was<br/>consistently being classified accurately at 98%. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/45339">http://10.250.8.41:8080/xmlui/handle/123456789/45339</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 | 08/09/2024 | 610 | SMME-TH-1044 | Thesis |