## 6 Taxonomy of Features for ECG Interpretation and Diagnosis
### Feature Extraction from ECG Signals
#### 6.2.3 Hybrid Features Extraction Approaches
False-positive R peaks were 0.0042 and 0.0044, respectively. False-negative R peaks were 0.0039 and 0.0078, while the absolute percentage errors were 0.40% and 0.71%, respectively. For the first method for R peaks detection, correctly identified R peaks of 99.16% and 98.90% were achieved for the two subjects, respectively. False-positive R peaks were 0.0031 and 0.013, respectively. False-negative R peaks were 0.0084 and 0.011, while the absolute percentage errors were 0.71% and 1.54%, respectively. These features were applied to develop the rule-based DSS. In the classification phase, 10-fold cross-validation method was used for testing based on different classification algorithms such as PART J48, Random Tree and Naive Bayes. Naive Bayes achieved the best performance in terms of mean absolute error and relative absolute error achieving 0.127 and 54.95%, respectively. [PERSON], [PERSON] [28] also presented a rule-based DSS for mHealth environments specifically for cardiac heart monitoring. The system detects and signals abnormal or emergency situations by using hybrid features based on contextual information such as the patient's posture and physical activity and data from a wearable ECG device. The rule-based DSS encodes high-level and specialized knowledge gathered from clinical experts for cardiac diagnosis and monitoring. The DSS makes inferences from a simple mobile reasoner to detect potentially dangerous situations. The features used by the system belong to five main categories: patient summary, heart monitoring information, physical activity information, posture information and alert information. Ontology was introduced to represent this information. The overall architecture of the proposed system is segmented into three different layers, namely: Data Layer, Decisional Layer and Action Layer. Data layer is responsible for collecting sensor data and representing them in the form of ontology description. The Decision layer is responsible for using the expert knowledge formalized in the ontology and production rules to recognize the possible critical situations and determine the suitable actions to be carried out by the Action Layer. Action layer executes the actions inferred by the Decision layer through the implementation of mechanisms to generate reactions such as alarms and feedback messages. To verify the system performance, the authors conducted a number of tests using a set of hardware devices. These include an all-in-one wireless ECG and activity monitor (i.e., Alive Heart Monitor device), a smartphone and a patient simulator (i.e., Datrend Advanced Modular Patient Simulator (AMPS-1)). They examined a set of 15 healthy people consisting of 80% men and 20% women with ages from 25 to 48 years. The experimental results show the feasibility of a context-awareapproach for cardiac monitoring and diagnoses. The proposed system was able to reduce the generation of unnecessary false alarms. Specifically, three records were prevented from misclassifying as false-positive alarms by the proposed system. The system achieved an accuracy of 80.00%. [PERSON] and [PERSON] [87] proposed the feature engineering method to extract hybrid features from ECG signals. In this study, four classes of arrhythmias were detected using a feature engineering technique that utilizes deep learning, fiducial points and K-NN. The features learned from pre-trained deep learning Convolutional Neural Network (CNN) architecture were used alongside handcrafted fiducial point-based features and K-NN features from the feature engineering approach. SVMS with different kernels and decision trees were employed to classify the features extracted by the proposed method. The model showcased very competitive performance results for beat classification and achieved an average accuracy of 99.77%, AUC of 99.99%, precision of 99.75% and recall of 99.30%. The strength and suitability of the proposed method is revealed in the high balance between sensitivity and specificity. The experiment was conducted using MIT-BIH dataset of 48 recordings from 47 patients.