TY - JOUR AU - Haider, Syed Irtaza AU - Alhussein, Musaed PY - 2019/08/07 Y2 - 2024/03/29 TI - Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier JF - Elektronika ir Elektrotechnika JA - ELEKTRON ELEKTROTECH VL - 25 IS - 4 SE - DO - 10.5755/j01.eie.25.4.23970 UR - https://eejournal.ktu.lt/index.php/elt/article/view/23970 SP - 47-57 AB - <p>An electrocardiogram (ECG) signal is usually contaminated with various noises, such as baseline-wander, power-line interference, and electromyogram (EMG) noise. Denoising must be performed to extract meaningful information from ECG signals for clinical detection of heart diseases. This work is focused on baseline-wander noise as it shares the same frequency spectrum as the ST segment of ECG signals. Hence, it is important to estimate the baseline-wander prior to its removal from ECG signals. This paper presents a method for classifying each segment of the ECG signal’s baseline-wander as minimal, moderate or large. We use the C4.5 decision tree algorithm to model the classifier using the WEKA data-mining tool. We test the proposed method on ECG signals obtained from the MIT-BIH arrhythmia database (48 ECG recordings, each slightly longer than 30&nbsp;min). We use 36 ECG recordings for training the classifier with the remaining 12 ECG recordings as the test data for classification. We partition each recording into 5 second, non-overlapping segments, which result in 361 segments for each record. The classification results show that the model classifier achieves an average sensitivity of 97.36&nbsp;%, specificity of 99.50&nbsp;%, and overall accuracy of 98.89&nbsp;% in classifying the baseline-wander noise in ECG signals. The proposed method effectively addresses the question of identifying the minimal baseline-wander segments. Moreover, the proposed framework may help in devising an algorithm for the selective filtering of moderate and large baseline-wander segments to achieve the best trade-off between accuracy and computational cost.</p> ER -