TWO-STAGE MODIFIED ENCODER FOR ECG HEARTBEAT CLASSIFICATION VIA FUSING MORPHOLOGICAL AND TEMPORAL FEATURES
Cardiovascular Diseases (CVD) are the leading cause of death in Indonesia and World. In Indonesia, the number of expert (cardiologists) limited and scattered unevenly. In the medical field, an electrocardiogram (ECG) is the gold standard for detecting heart disease. The focus of this study was to...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/71775 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Cardiovascular Diseases (CVD) are the leading cause of death in Indonesia and
World. In Indonesia, the number of expert (cardiologists) limited and scattered
unevenly. In the medical field, an electrocardiogram (ECG) is the gold standard
for detecting heart disease. The focus of this study was to build an Artificial Neural
Network-based model capable of accurately classifying arrhythmias from ECG
signals. Physionet MIT-BIH (The Massachusetts Institute of Technology – Beth
Israel Hospital) Database used to train and evaluate the model. A Two-Stage
Modified Encoder (TSME) is proposed as a model for the arrhythmia classification
system. This model uses two inputs: 187 samples were extracted from
morphological waveforms, and 8 temporal features were calculated from the R-R
interval of heartbeats. Input from morphological waveforms processed with
convolution block and attention block, then concatenated with input from temporal
features. The proposed method was evaluated with inter-patient paradigm to
conform to the AAMI standard, where the subject (record) differentiated during the
training and testing phases. The obtained sensitivities of Normal (N),
Supraventricular (S), Ventricular (V) and Fusion (F) were 94.24%, 90.02%,
92.89% and 18.56%. Positive predictivity values of the model are 99.79%, 43.55%,
88.59% and 14.49% for N, S, V and F class. The overall accuracy of four classes
of heartbeats is 93.41%. The comparison shows that our proposed methods have
achieved competitive performance results compared to state-of-the-art. To assess
how well the model generalized the problem, the INCART Database was evaluated
as a different source of data. The method was suitable for clinical application as
both a high positive predictive value for the N class and high sensitivity for the
abnormal classes (S and V). |
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