**7. Conclusions**

Health issues in the human race are increasing day by day and cardiac issues are one of the most common diseases which has been noticed in the past few decades. Therefore, many technologies have been introduced and CAD is the most emerging technology to diagnose cardiac issues or solve heart-related diseases. Furthermore, deep learning has played an important role in the area of computer-aided diagnosis (CAD). From the above discussion, it can be observed that various algorithms or methods have performed pretty well in the field of cardiovascular disease detection. This indicates that deep learning in cardiac signal processing has an unbounded scope in the research field for enhancing CAD and getting more accurate and cost-effective and fast output.

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

**Figure 11.** *Confusion matrix [35]. (a) 7 heartbeat classes. (b) 5 heartbeat classes.*

**Figure 12.**

*Comparison of performance indexes of DNN and the average performance of cardiology students on the test set [36].*

#### **Figure 13.**

*Comparison of the accuracy of the different ECG classification techniques [34].*
