## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Feature Extraction from ECG Signals
#### 6.2.2 Non-Fiducial Features Extraction Approaches  
Dissimilar to 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. Non-fiducial approach was introduced to resolve the shortcoming of the fiducial point-based feature extraction approach. As we oppose fiducial point-based approaches, non-fiducial methods do not require determination of the precise boundaries of the waveforms. This approach extracts differential data within ECG waveforms with no conspicuous fiducial points [32,73]. Although, studies have shown that both fiducial and non-fiducial approaches exhibit similar results in terms of performance based on a comprehensive performance evaluation study conducted by [PERSON], [PERSON] [128]. However, more research effort is needed to validate this empirical finding. In essence, non-fiducial approaches employ a windowing method where ECG signal in time-domain is decomposed into windows using the time interval as the possible minimal manipulation that can be performed on the signal. Each segmented window is then subjected to a transformation to extract a number of coefficients, which will lead to the feature of interest. These features can be used to detect the rate of heartbeat. A typical example of window segmentation for non-fiducial approaches is shown in [32]. In this figure, the ECG signal is partitioned into segments and each segment is studied separately. Majority votes of the detection result obtained from each window can be used to categorize the patient [73]. In the data stream environment, the transformed windows can be subjected to buffer and unbuffered approaches. [PERSON], [PERSON] [32] proposed ECG stream method to extract non-fiducial features from ECG signals. They proposed a continuous authentication ES by considering ECG signals as continuous data streams. The dataset for the experiment is based on 10 individual records selected from MIT-BIH Normal Sinus Rhythm database. K-NN algorithm, with K = 1, was used to perform the final classification of the patient's health status. In their study, Walsh-Hadamard transform is then utilized to transform the resultant windows. This transformation performs a projection of a signal onto a set of square waves, which is referred to as Walsh function. Performance accuracy of 96% was reported from the experimental investigation. Despite the impressive results, the proposed algorithm cannot be generalized as the system's performance was only evaluated on 10 records of individuals. The performance of different non-fiducial approaches such as percent residual difference, correlation coefficient, wavelet distance indices, ECG phase space, kernel-based method, cross-correlation analysis and ECG morphological features has been reported in the literature [6, 73]. For instance, the performance of three non-fiducial approaches has been explored in the work of [129]. These approaches include distance measurement using wavelet coefficients, correlation coefficient and percent residual difference. The experiment was conducted on 50 subjects and an accuracy of 89% has been achieved. The authors reported that the approach based on distance measurement using wavelet coefficients outperformed the other two approaches. However, despite the remarkable success achieved in this study, the detection accuracy still needs further improvements, and the proposed approach has been characterized to be highly sensitive to the ECG segment length [6]. Distance measurement using wavelets has also been studied in the work of [129] and [130] as reported in [73]. [PERSON], [PERSON] [129] proposed a framework that is based on non-fiducial feature extraction approach with the aid of wavelet transform distance. The study analyzed 50 subjects and reported an accuracy of 89%. [PERSON], [PERSON] [130] proposed an individual identification technique with 512-point feature extraction using Euclidean classifier and wavelet transform.