## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Noise and Noise Cancellation Techniques for ECG Signals  
There are two main classes of noise that affect ECG signals. These include low frequency baseline wander and high frequency random noises [30]. The former is caused by the movement of the respiratory tract and body movements. The latter, high frequency random noises are caused by mains interference, which is 50 or 60 Hz [123]. In addition, the causes can also include muscular activity, motion artefacts, random shifts of the ECG signal amplitude usually created as a result of poor electrode contact and body movements and other interruptions of the original recorded ECG signal [18; 30; 79]. To eliminate low frequency baseline wander, linear filtering and polynomial fitting (cubic spline filtering) techniques have been used [30]. In linear filtering, a time-invariant high-pass filter is developed, and a cutoff point is chosen to preserve clinical details in the ECG, as well as guarantee the removal of baseline wander. The lowest heart rate at around 40 bpm is chosen during bradycardia, which specifies around 0.67 Hz--the lowest frequency component in the ECG signal. The cutoff frequency can then be set at around 0.5 Hz. Low-pass filter has been widely used to suppress power line interference [79]. [PERSON] and [PERSON] [73] proposed Daubechies 4 (db4) wavelet to extract baseline wander and noise from the original ECG signals. [PERSON], [PERSON] [74] proposed Discrete Wavelet Transform (DWT) of Coif3 family with the Rigrsure rule of the hard thresholding technique to denoise ECG signals. A pass-band filter with frequency values between 0.67 Hz and 45 Hz has been used to remove respiration and power line noise in the ECG signals [32]. Conversely, polynomial fitting, also referred to as cubic spline filtering, involves the fitting of polynomials to representative points (knots) on the ECG, where one knot is used for each beat. These knots are chosen from a \"silent\" segment, such as the PQ interval. This fitted polynomial passes through each knot in a smooth fashion [30; 85]. In biomedical and telemedicine domains, the wireless transmission of ECG signals is mostly affected by noise owing to improper channels. This noise is modeled as white Gaussian noise, which can be regarded as a conventional frequency noise. The noise is added to the pure ECG signals and requires a filtering technique for effective removal. A number of filtering techniques for white Gaussian noise have been studied. These include the application of Wiener and adaptive filtering techniques [124]. Wiener filter is an optimal filter that is most useful when the power spectrums of the input signal and noise overlap and cannot be separated by the traditional low-pass filters. Despite Wiener optimalperformance, its computational complexity remains a major challenge [125]. Adaptive filter was introduced to provide better performance over Wiener filter. Different types of adaptive filtering techniques such as Wavelet, Empirical Mode Decomposition (EMD) and Recursive Least Squares (RLS) have been explored to remove noise in ECG signals [58]. Stochastic gradient Mean Square (LMS)-based adaptive filtering has been used for white Gaussian noise removal to reduce energy consumption in signal processing [85]. However, LMS-based adaptive filtering requires a large step size to produce good filtering performance. One of the major setbacks of the adaptive LMS algorithm is the fixed step size parameter requirement for every iteration, which greatly affects the filtering performance [18,124]. To improve the performance of the filtering process, an LMS algorithm with normalized step size is proposed in a number of studies [18]. [PERSON], [PERSON] [18] introduced a delayed error normalized LMS (DENLMS) adaptive filter algorithm for ECG noise cancellation with moderate computational complexity. DWT has been used in research work of [67] to denoise ECG signals. Preprocessing of ECG signals has attracted research attention in various bioinformatics applications.