# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 6 Applications in ECG Processing
### Recent Interest in Deep Learning for ECG
A complete end-to-end 1D CNN model was designed instead of the hand-crafted feature extraction and selection used in traditional methods, and this model achieved an overall accuracy recognition of 91.33% for 17 cardiac arrhythmia disorders. Another method was proposed based on a 13-layer CNN with residual skip connection for the classification of heartbeats [199], in order to accurately differentiate five different arrhythmias, and subsequently to transfer the knowledge acquired on this task (transfer learning) to the myocardial infarction classification task. The proposed method was evaluated on 10-s windows of signals from the MIT-BIH Arrhythmia and the PTB Diagnostic ECG Database. The suggested method was able to make predictions with accuracy of 93.4% and 95.9% on arrhythmia classification and myocardial infarctionclassification, respectively. Furthermore, authors presented visualizations of the high-dimensional vector created by the last convolutional layer in the 2D space using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method. There are also many solutions that use RNN architectures for cardiac arrhythmia detection. Normal and abnormal beats were classified by implementing LSTM and GRU architectures [200], aiming to enable the automatic separation of regular and irregular beats on the MIT-BIH Arrhythmia Database, and comparing different RNN models in order to show their effectiveness, accuracy, and capabilities. Mixed architectures have also been proposed. An automated system that uses a CNN and RNN LSTM was proposed in [201] for diagnosis of normal sinus rhythm, left bundle branch block, right bundle branch block, atrial premature beats, and premature ventricular contraction (PVC), all of them from the MIT-BIH Arrhythmia Database ECG signals. The novelty of this work is that ECG segments of variable length were used, achieving 98.10% accuracy, 97.50% sensitivity and 98.70% specificity for ten-fold cross validation strategy. A simpler solution was implemented [182] in terms of a common Feed-Forward Neural Network approach for ECG beat classification, conducting the experiments on the MIT-BIH Arrhythmia Database, and, although a very simple architecture was used, good results were obtained in terms of efficiency, accuracy, sensitivity, and specificity. Diabetes detection has been also addressed as another task derived from ECG signals. The changes in the RR-interval signals, known as heart rate variability (HRV) signals, have been pointed to be potentially useful for this purpose [202], in terms of a methodology for classification of diabetic and normal HRV signals using LSTM, CNN, and their combinations for extracting complex temporal dynamic features from the input HRV data. The obtained performance improvement was 0.03% and 0.06% in CNN and CNN-LSTM architectures, respectively, compared to 95.7% accuracy in their earlier work. Finally, QRS complex detection is very relevant to obtain additional useful clinical information from the ECG signals, including the subsequent wave delineation process also providing RR intervals, QT intervals, and PR intervals, so its detection is critical for ECG-based health evaluation. A 1D CNN was delivered for QRS complex detection [203], consisting of levels (object-level and part-level) for extracting different grained ECG morphological features. All of the extracted morphological features were used by a Feed-Forward Neural Network for the final QRS complex detection. Based on the MIT-BIH Arrhythmia Database, the proposed detection method overall achieves 99.77% sensitivity, a 99.91% positive predictive rate, and a 0.32% detection error rate.