## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Feature Extraction from ECG Signals
#### 6.2.1 Fiducial Features Extraction Approaches
The authors applied Linear Discriminant Analysis (LDA) to classify 29 subjects. The results show that 100% subject recognition is possible using the extracted fiducial features. 82% ECG beat recognition rate was achieved using this approach [73]. Thirty (30) fiducial point features have been proposed in the work of [PERSON], [PERSON] [127]. The authors applied Soft Independent Modeling of a Class Analogy (SIMCA) classifier for comparison of unlabeled samples with the data pertaining to a trained group. This method was proposed for classification of a subject with theappropriate well-matched class label. The experiment was carried out using a total of 20 subjects and a recognition rate of 100% was achieved [73]. In most of the existing rule-based ES for ECG interpretation, clinicians are not able to externally access the rules used by these systems. As a result, the ES and DSS become black-box. In order to solve this problem, [PERSON], [PERSON] [31] proposed a number of fiducial-based features by studying intervals, segments, peaks, widths and some features based on heart rate and the cardiac axis to build a rule-based eXtensible Markup Language (XML) framework for ES, which can be used for interpretation of ECG signals. The rules mined from ecgRuleML proposed in this study can be accessed externally by clinicians to have an understanding of the underlying decision rules used by the ES when diagnosing patients with heart-related diseases. An ecgRuleML document represents readable (both to human and machine) rule-based expert knowledge. DCT and DWT have been applied in the work of [58,67] to process R peak data extracted using Pan-Tompkins R peak detection algorithm. By analyzing ECG signals obtained from MIT-BIH arrhythmia database, Daubechies (db4) mother wavelet-based multiresolution analysis has been applied to decompose the signal into nine stages of sub-bands. The ninth stage approximation coefficient of the frequency band between 0 and 0.351 Hz refers to the baseline drift. Since the ECG signal does not have any important data above 45 Hz, the initial two levels of the wavelet coefficients are set to 0. The remaining wavelet coefficients in the detailed sub-bands (third to ninth levels) are reconstructed to get the resultant filtered ECG. The R peak extracted data are divided in such a way that the individual division contains 99 and 100 samples before and after the R peak, respectively. ECG beats of 200 samples, which belong to five categories of arrhythmias have been utilized by the authors [58]. Moreover, certain numbers of abnormal features are difficult to represent numerically and as such the characteristic element can be used instead to depict abstract features such as M-shaped P waves and slurred S waves [32]. While the use of fiducial features has helped in studying the various significant points in the ECG signals, this approach requires precise detection of boundaries. Obtaining effective boundaries has been challenging due to the variations in ECG signals and the presence of different types of noise. In addition, this approach has been reported to be computationally challenging [32]. As such, the use of non-fiducial feature extraction approaches has been extensively studied. **Findings:** Several studies have considered the use of fiducial-based approaches for feature extraction from ECG signals, which enable researchers to build models that consider different combinations of P, Q, R, S and T waves in the time-domain. It is believed that these salient points capture ECG signature of individual patients and based on this assumption, ES and DSS solutions are developed. The ability to extract these salient points has been explored to calculate important feature parameters for ECG interpretation and diagnosis. Hence, different approaches have been proposed to extract these salient features as highlighted in this section.