{'fraction_non_alphanumeric': 0.022189349112426034, 'fraction_numerical': 0.0, 'mean_word_length': 4.934210526315789, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
ECG Signal Noises Versus Filters for Signal Quality Improvement

One of the major diseases that cannot predict at the right time and lead to instantaneous death among humans is heart disease. Heart-related problems can be identified and detected using Electrocardiogram (ECG) signals obtained from the human body. So, the importance of analyzing and classifying the ECG data is high because several medical industries are focusing on this problem. Since ECG data is continuous, voluminous, and extracted from the ECG signal, pulling more and all the features are essential. In various traditional methods, the accuracy is less, and decision-making based on the features may fail. Decision making or classification accuracy is entirely depending on the ECG signal quality. The signal quality is improved by pre-processing the input signals, and it is explained in this paper. This paper presents various noises that affect the ECG signal quality and the reason for the noises. Filtering the noise components is a highly challenging problem. Different filtering methods can do it are implemented to remove the noise from the ECG data. The performance of the filters is evaluated by calculating Signal-to-noise-ratios (SNR) and compared with one another. The experiment is carried out on MATLAB software, and the comparison results are given in the paper.