TY  - BOOK
AU  - Nayyab, Rida
AU  - Supervisor : Dr. Muhammad Asim Waris
TI  - Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning
U1  - 610 
PY  - 2024///
CY  - Islamabad : 
PB  - SMME- NUST; 
KW  - MS Biomedical Sciences (BMS)
N1  - Cardiovascular diseases are considered the major cause of death worldwide surpassing
cancer. However, despite the broad category of diseases, research has been limited to
binary classification i.e. normal and abnormal class leaving behind the accurate
classification of specific diseases that affect the ECG waveform. PTB – XL database
offers a wide variety of ECG records, but little research is dedicated to extracting
morphological features for multi-class classification. Therefore, the paper used the open
database to filter the ECG signal records having single unique labels and pre-processed
them using the Butterworth bandpass filter and DWT db8. The Bandpass filter corrected
baseline wander and reduced noise however, a high signal-to-noise ratio was achieved
after applying 8-level DWT. The processed signals were fed into the Pan-Tompkins
algorithm to extract R peaks. These peaks served as a baseline to identify other
morphological features i.e. P-QRS-T intervals and amplitudes. These extracted features
were labelled into 1 normal and 4 abnormal classes. There was a class imbalance in the
dataset that could cause bias while training models. Therefore, SMOTE-NC was applied
to upsample the dataset. The new dataset was split into the training set and the testing set.
These sets were given as inputs to CNN and DNN models for a 5-fold loop. The
performance was evaluated for both models using metrics like F1 score, recall, precision
and accuracy. The CNN model achieved a mean accuracy of 81% whereas the mean
accuracy for DNN was 84%. It was also noted that among the 5 classes, HYP was
consistently being classified accurately at 98%
UR  - http://10.250.8.41:8080/xmlui/handle/123456789/45339
ER  -