## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Feature Extraction from ECG Signals
#### 6.2.1 Fiducial Features Extraction Approaches  
Fiducial feature extraction approach extract features from the physiological signals. Fiducial-based approaches are techniques used to extract the characteristic points of the ECG signals in time-domain [55]. ECG normal signal contains specific waves, consisting of P, QRS complex and T waves. Heart atria depolarization is depicted by P wave; a positive, low-frequency and low amplitude signal. Conversely, ventricle depolarization is depicted by the highest amplitude and the highest frequency of ECG waves, as described by the QRS complex. At the end of the cardiac cycle, the T wave is used to depict ventricle repolarization. These waves form the basis for the distinctive points of the ECG-recorded signal, which are P, Q, R, S and T. To extract the salient fiducial features for effective ECG interpretation and diagnosis, the onset, offset, amplitude, duration and gradient of each wave are explored [6]. When dealing with fiducial feature extraction, the algorithm used for extracting the salient characteristics of ECG signals must determine the precise boundaries of the waveforms. Based on the suitable combination of P, Q, R, S, T waves, several features of ECG can be determined. For instance, [PERSON], [PERSON] [37] extracted six fiducial-based features from ECG basic waves (i.e., P, Q, R, S and T). The extracted features: P-R, QRS, R-R time intervals, heart rate, R-R time interval variation and T-P and S-T interval voltage levels, were used to train a fuzzy classifier implementing Takagi-Sugeno-Kang (TSK) fuzzy inference system based on 48 ECG signal records. The proposed classifier was used to detect 14 classes of arrhythmia [37]. ECG signal features consisting of P, Q, R, S, T waves and the zero level of the signal are extracted first with the use of wavelet transform. To extract the fiducial features from ECG waveforms, R peak (QRS complex) detection is very crucial. The detection of R peak will help in the calculation of two important feature parameters. These parameters are interbeat (RR) interval and the Heart Rate Variability (HRV). The abnormality of heart rate or its variations, which exists in the morphological pattern, is a sign of an arrhythmia. This variation in the morphological pattern can be detected through the analysis of recorded ECG waveforms. HRV is the most powerful and useful feature extracted from ECG signal variations for detecting cardiac disorders and associated diseases [18]. The most widely used R peak detection algorithm in the literature is Pan-Tompkins algorithm [30; 58; 79]. This algorithm involves analysis of the slope, amplitude and width of QRS complexes in the ECG signals. It includes several filtering techniques that accomplish high-pass, low-pass, squaring, derivative, integration, search operations and adaptive thresholding. [PERSON], [PERSON] [79] proposed polygonal approximation method to extract fiducial features. [PERSON]'s method for fiducial feature extraction is based on a threshold value of the auxiliary signal produced from Hilbert transform. The popularity of DWT has made it useful in the analysis of ECG signals. The DWT-based R peak detection technique has been employed specifically for the reduction of the computational complexity in the feature extraction phase of ECG interpretation [18]. Several wavelets transforms have also been used for the detection of R peaks in ECG signals. However, in most of the feature extraction processes, selecting the mother wavelet is very vital to improve performance. For instance, [PERSON], [PERSON] [18] selected Coiflet wavelet to detect QRS complexes in ECG signals. Coiflets are wavelets formed from Daubechies wavelet family, which has a closed resemblance to QRS complexes and possesses low frequency energy spectra [37]. Coiflets are more symmetric in nature with a near linear phase and are characteristically better than Daubechies wavelet and Spline wavelet [18]. [PERSON], [PERSON] [126] extracted 15 fiducial points from ECG signal after detecting P, QRS complex and T waves.