## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Feature Extraction from ECG Signals
#### 6.2.2 Non-Fiducial Features Extraction Approaches
Recognition rate of 100% was reported on 35 subjects belonging to the normal class and 81% recognition rate for 10 subjects belonging to the arrhythmia class. Similarity, as well as dissimilarity measures between ECG phase space portraits, has been studied for the development of human identification ES. The system achieved an accuracy of 99% using a three-lead ECG configuration. However, it was reported that when the number of leads was reduced to one, the accuracy of the system dropped significantly to 93% [6]. A tunable Q-factor wavelet transform (TQWT) and spectral feature analysis were proposed in [64] to build a DSS for automatic sleep staging based on electroencephalography (EEG) signals. The authors split EEG signals into wavelet sub-bands using a tunable output factor and spectral characteristics derived. The study applied wavelet transform for extraction of features and used a random forest technique for the grading stage. The authors used time-frequency features to extract specific spectral features from sub-bands of the TQWT. The experiment was conducted on 28 subjects and an accuracy of 90.38-97.50% was reported for 6-class and 2-class, respectively. In the field of identity recognition using ECG as a physiological trait for security, [74] proposed autocorrelation and kernel-based approaches for feature extraction and dimensionality reduction of ECG signals. The features extracted using this approach were used to train a one-class Support Vector Machine (SVM) to learn the individual pattern in the ECG signals. The autocorrelation method is applied on individual nonoverlapping windows to detect discriminative feature vectors. The ECG data have been windowed into 1028 coefficients at 5 s duration. For reducing the dimensionality of these feature vectors, a different dimensionality reduction method was evaluated, which includes kernel-based PCA, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The reduced features were from the one-class SVM for subject classification. The authors reported that Kernel-based PCA produces the lowest error when tested on both binary and one-class SVM. It has been reported that during the windowing section of non-fiducial feature extraction approach, unnecessary signals may be present other than those normal ECG signal, leading to a reduction in the overall recognition rate. This may also affect the individual window identification rate. The presence of these distorted signals can be solved by analyzing all windows generated and removing those windows that may be difficult to recognize or those windows that have unwanted signals. To address this issue, [PERSON] and [PERSON] [73] proposed the window removal approach, which applied DCT to all generated windows. The threshold value is used to filter windows that do not certify the condition. After removing the unnecessary windows, the extracted non-fiducial features were used to train three classification algorithms (Nearest Neighbor, SVM and LDA). An experiment was conducted using Normal Sinus Rhythm, PTB Diagnostic and QT databases. This research shows that the recognition rate of both subject and window can be improved by removing unnecessary windows and signals from the decomposed ECG signals. **Findings:** Contrasting the time-domain analysis used in the fiducial approaches, non-fiducial feature extraction techniques obtain features from a transformed domain using Fourier or wavelet analysis. The feature extraction techniques were introduced to solve the problems of fiducial-based approaches. This technique can extract features from ECG waveforms with no conspicuous fiducial points. Research on non-fiducial approaches has shown significant performance improvement. Although studies have shown that both fiducial and non-fiducial approaches exhibit similar results in terms of performance, more research effort is needed for robust empirical validation, although there is still a need to consider a large number of ECG samples to evaluate the generalizability of these approaches.