Denoising and feature extraction of electrocardiogram (ECG) signals
An Electrocardiogram (ECG) signal is often contaminated with noise artifacts such as baseline wandering, powerline interferences and electromyographic interferences (EMG). These noises and artifacts present in the ECG signal make it difficult for doctors and nurses to perform clinical diagnosis to i...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/71558 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | An Electrocardiogram (ECG) signal is often contaminated with noise artifacts such as baseline wandering, powerline interferences and electromyographic interferences (EMG). These noises and artifacts present in the ECG signal make it difficult for doctors and nurses to perform clinical diagnosis to identify diseases such as cardiac abnormalities. To tackle this problem, we have implemented the Discrete Wavelet Transform (DWT) method to remove the above-mentioned noise artifacts from a noisy ECG signal. We have also included in additional features such as the R-peak detection, heart beat calculation features and identification of the type of Arrhythmia disease (Bradycardia, Tachycardia). The ECG signals were obtained from the MIT-BIH Arrhythmia Database while the real baseline wandering noise (‘bwl’) was obtained from the MIT-BIH Noise Stress Test Database. The results obtained reflect that the DWT method is an effective and efficient method to filter out the noise from the noisy ECG while protecting the morphological features of the ECG signal. The ECG signals that we would be focusing on in this report would be that of Arrhythmia patients. |
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