# 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  
Considering the vast literature in the separate fields of DL and ECG processing, we have not found specific reviews jointly covering both topics. However, there are many works about DL and biosignals where applications in ECG are covered. The latest scientific research on DL methods for physiological signals was compiled in [50], including Electromyogram (EMG), Electroencephalogram (EEG), Electrooculogram (EOG), and a part dedicated to ECG. A survey on DL techniques for 1-dimensional biosignal analysis [22] paid attention to computer-aided diagnosis and ECG analysis among them. Finally, an excellent review and discussion were conducted [53] regarding the use of the ECG in biometrics, where DL is already mentioned. What can be readily found today are ML reviews focusing on ECG applications, with some sections that briefly deal with DL. Several ML approaches (Neural Networks among them) were analyzed [177] for diagnosing myocardial infarction, differentiating arrhythmias, and detecting hypertrophy by using ECG signals. The computational methods in use for ECG analysis have been described [179], with focus on ML and three-dimensional computer simulations, as well as on their accuracy, clinical implications, and contributions to medical advances. The relevance of DL is scrutinized therein by reviewing a few related papers. A survey of ML algorithms for ECG classification into arrhythmia types was presented in [183], and, among all the different classifiers, the ANNs are stressed because of their popularity. Lastly, the methods of ECG processing have been reviewed from a pattern recognition perspective [197], with particular focus on ANNs and Support Vector Machines. In contrast with the lack of specific and full reviews, the literature about applications of DL to EGC has recently become an immense ammount. We present next a summary of the state of the art about this topic. To do this, we used PubMed(tm), IEEEXplore(tm), Google Scholar(tm), and ScienceDirect(tm) portals and search engines to search for publications related to DL applications in ECG. The searched keywords included \"deep learning\", \"neural network\", \"electrocardiogram\", \"ECG\", \"convolutional neural network\", \"CNN\", \"recurrent neural network\", \"RNN\", and \"LSTM\". We only present here the most relevant papers in our view that were published between 2017 and 2018. Arrhythmia detection and classification are some of the most popular topics in ECG, since the automatic recognition of abnormal heartbeats from a large amount of ECG data is an important and essential task. A huge number of methods have been proposed to address the problem of ECG beat classification. However, most solutions in the last two years turned to using CNN and RNN architectures. For instance, a relevant CNN-based system [181] developed a 9-layer 1D CNN to automatically identify five different categories of heartbeats, namely, non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. The experiment was conducted in original and noise attenuated (filtered to remove high-frequency noise) sets of ECG signals derived from the MIT-BIH Arrhythmia Database. This set was artificially augmented to compensate the number of instances in the five classes of heartbeats, though the augmentation process was unclearly described. The CNN was trained using only thousands of beats belonging to augmented data set and it achieved 94.03% accuracy in the noisy dataset and 93.47% in the noise-free dataset. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07% and 89.3% in noisy and noise-free sets, respectively. Another DL approach for cardiac arrhythmia detection was presented [198] based on ECG signal analysis, using 1000 10-s ECG signal fragments again from the MIT-BIH Arrhythmia Database.