## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Feature Extraction from ECG Signals
#### 6.2.3 Hybrid Features Extraction Approaches  
ECG interpretation and diagnosis in medical applications have been shown to be very difficult due to the underlying characteristics of ECG signals. As such, a single feature extraction technique may not be sufficient when analyzing all manner of ECG data. Consequently, researchers have explored the possibility of performing hybrid feature extraction to improve the resultant classification performance of the recognition algorithms. To develop a robust ES and DSS, ECG signal analysis requires appropriate features and classifiers to categorize patient health status. For instance, [PERSON], [PERSON] [55] has combined ECG characteristic points with patient's demographic data as input to a web-based DSS to detect a patient suffering from heart problems. Cardiac arrhythmia data obtained from the University of California was used to evaluate the system. A dataset of 279 attributes and 452 instances. which are spread among 16 types of arrhythmia classes, was developed. Five classifiers were used to classify the different classes of arrhythmias considered in this study. These include J48 decision tree algorithm, Jrip rule-based algorithm, K-star nearest neighbor algorithm, SVM and Artificial Neural Network (ANN). The authors applied Synthetic Minority Oversampling TEchnique (SMOTE) filter in WEKA to produce duplicates of the minority classes. J48 algorithm has shown the highest classification accuracy of 98.29%. [PERSON], [PERSON] [18] has explored the possibility of combining fiducial features with non-fiducial by extracting 14 time-domain and frequency-domain HRV features from ECG signals. In essence, three important frequency regions are present in the human HRV signal. These include high frequency (HF), low frequency (LF) and very low frequency (VLF). VLF is below 0.04 Hz. LF varies between 0.04 and 0.15 Hz and HF is from 0.15 to 0.5 Hz. Six frequency-domain features were added to the eight time-domain features to identify abnormal heart rhythms. The frequency-domain features, which were extracted using NI LabVIEW biomedical kit include LF Power, HF Power, VLF Power, HF norm, LFnorm and ratio of LF to HF. Experimental results based on SVM classification algorithm yielded a maximum accuracy of 96% for classifying normal and abnormal subjects based on the ECG dataset collected from MIT-BIH database. [PERSON], [PERSON] [30] has combined frequency-domain features such as LF, HF and the ratio of LF to HF, along with features collected from various wearable sensors to develop a rule-based DSS for heart disease diagnosis. The authors developed a system that collects and analyzes information applicable for predicting the severity of a health condition of patients suffering from chronic diseases using wearable sensors. The system used multiparametric sensor data which include ECG, motion, oxygen saturation (Sp02), respiration and temperature, among others. A smart assistant device was integrated which is responsible for analyzing the collected multiparametric data and simplifying the monitoring of patients. After preprocessing, the smart assistant device integrates all collected data from sensors with the patient's information. The authors evaluated the feature extraction stage using two recordings with each less than 2 h. These samples were taken from two subjects, and their annotation was performed by a physician. Three R peak detection methods were tested, and evaluation metrics such as a correctly identified R peak, false-positive R peak, false-negative R peak and absolute percentage error were used. For the first method for R peaks detection, correctly identified R peaks of 99.62% and 99.40% were achieved for the two subjects, respectively. False-positive R peaks were 0.0063 and 0.0060, respectively. False-negative R peaks were 0.0038 and 0.0060, while the absolute percentage errors were 0.58% and 0.86%, respectively. For the second method for R peaks detection, correctly identified R peaks of 99.61% and 99.20% were achieved for the two subjects, respectively.