**6. Discussion on the experimental results based on the current state-of-the-art techniques**

**Figure 9a** and **b** demonstrate the DENS-ECG model's confusion matrices for the 5 fold CV and test set, respectively. The no wave class has the majority of incorrect cases in all three classes which are P-wave, QRS, and T-wave or it can be said that the model does not make significant errors in classifying the three major classes (P-wave, QRS, and T-wave). The minimal discrepancy between the 5-fold CV and test outcomes indicates that the model has been effectively trained and does not have an overfitting problem.

*Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders DOI: http://dx.doi.org/10.5772/intechopen.103075*

**Figure 9.** *Confusion matrix [33]. (a) 5-fold Cross Validation. (b) Test set.*

As demonstrated in **Figure 10** the performance plot, the DENS-ECG model performs similarly to other models in QRS detection with 99.61% of sensitivity and 99.52% of precision. The wavelet-based model proposed by Martinez et al. has the best performance in terms of sensitivity and accuracy of 99.8 and 99.86%, respectively followed by Kim and Shin's proposed model. The postulated DENS-ECG model performed similarly to the well-known Pan and Tompkins's QRS detection model but it outperformed the QRS detection methods proposed by Poll et al.

In [35], the classification ECG signals from heartbeat were classified into both 7 and 5 arrhythmia classes, respectively. For 5-classification problems, the heartbeats are divided into five categories by the Association for Advancement of Medical Instrumentation (AAMI). normal (N), supraventricular (S) ectopic, ventricular (V) ectopic, fusion (F), and unknown (Q) beats are the four types of an ectopic heartbeat.

**Figure 10.** *Comparison of DENS-ECG and various deep model architectures' classification performance on the test set [33].*

Further, the class N is divided into three more classes in the 7-classification to improve resolution by isolating the two conduction anomalies known as left bundle branch block (L) and right bundle branch block (R). **Figure 11** represent the confusion matrix of 7 and 5-class classification problem, respectively where the former model is capable of effectively distinguishing L and R from N.

As shown in **Figure 12**, Ribeiro et al. [36] has compared DNN's performance indexes to the average performance of 4th-year cardiology residents, 3rd-year emergency residents, and 5th-year medical students. The performance of the DNN on the test set is demonstrated in the above accuracy plot. The above-shown figure shows that the performance of DNN which exceeds human performance. In most cases, the accuracy of DNN on the data set is more than 95%.

Finally, the work of Jambukia et al. [34] presents a survey on the performance of various works present in the literature which are based on ECG signal categorization utilizing different pre-processing approaches, feature extraction techniques, and classifiers. **Figure 13** presents the plot of the accuracy of different ECG classification techniques which have used the MIT-BIH arrhythmia database over time.
