Deep neural network model for hypertension risk level detection using ECG signal
Background and Objectives: Early detection of hypertension risk is crucial as it affects about 1 in 4 adults and accounts for about half of all heart diseases and stroke-related deaths globally. Traditionally, assessments can be time-consuming and prone to human error. This study proposes an Elec...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/175003 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Background and Objectives: Early detection of hypertension risk is crucial as it affects about 1 in
4 adults and accounts for about half of all heart diseases and stroke-related deaths globally.
Traditionally, assessments can be time-consuming and prone to human error. This study
proposes an Electrocardiogram (ECG) based model for hypertension risk level detection.
Methods: Hypertensive ECG recordings from SHAREE and Normotensive ECG recordings from
PTB-XL were utilized. These ECG recordings undergo Daubechies-6 wavelet transform and a
median filter to denoise and prepare the data for a deep convolutional network. The deep
convolutional network consists of 1D convolution and max pooling layers, which extract feature
mappings and perform risk level classifications. It classifies the ECGs into ‘normotensive’ (no
risk), ‘hypertension’ (low risk), and ‘hypertension + any recorded event within 12 months of
recording’ (high risk).
Results: The multi-class classification achieved a micro-average accuracy of 99.59%, indicating
excellent overall performance. Individual classification precision ranged from 97.8% to 99.74%
while recall exceeds 97.31% for all categories. The high accuracy and strong individual
classification performances demonstrate the effectiveness of the proposed model in accurately
identifying hypertension risk levels.
Conclusion: This study demonstrates the potential of the proposed deep neural model for
hypertension risk level detection. However, a more clinical approach would still be required to
validate the model’s reliability. |
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