# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 6 Applications in ECG Processing
### Open Issues in Deep Learning for ECG  
Note that, in all of the previous works, publicly available databases, like the MIT-BIH Arrhythmia Database (often) or the PTB Diagnostic ECG Database (sometimes), although they are undoubtedly valuable, should still be noted as not being initially designed for training ML systems. Therefore, their use might be not enough for these types of applications. The small number of records of unique patients and the unbalanced classes are some examples of this. Some works apply techniques that are usually learned from the use of DL to image or audio databases to try to solve these challenges, which however they can not be straightforwardly applied to the ECG-based problems. For instance, data augmentation for low-represented arrhythmias can be very misleading, and it is not so straightforward as in image systems. In addition, the generalization of the system must be taken into account, as beats in the same patients are highly repetitive, so complete patients should be left separate to perform the validation and the test stages, which in too many cases is not reported, and very likely it is not done. This could be the cause for the high performance values obtained from recent DL systems, which in fact are similar to the performance obtained in the ML literature for ECG processing, sometimes due to the same bias in the machine training. Another problem that should be pointed is that most of these works are not addressed with the support of cardiologists in the research team, which leads to poor definitions of the clinical applications. In [204], an algorithm was developed that is said to exceed the performance of cardiologists in detecting a wide range of heart arrhythmias from ECGs recorded with a single-lead wearable monitor. Authors trained a 34-layer CNN which mapped a sequence of ECG samples (30-s long raw signal) to a sequence of rhythm classes. A proprietary dataset of 91,232 single-lead ECGs from 53,549 unique patients (compared with 47 from MIT-BIH Arrhythmia Database) with 14 classes (12 heart arrhythmias, sinus rhythm, and noise) was used. The performance of the model exceeded the average cardiologist (average of six cardiologists) performance in both recall (sensitivity) and precision (positive predictive value). This study can be pointed as an excellent reference work in the field, as it does not exhibit any of the pointed problems, so the results can be considered as solid and a reference for comparison of subsequent works. In any case, the previous compilation reveals that cardiac signal analysis has become in an extremely short time an application field for DL techniques. Whereas it is undoubtedly a probably mandatory scenario to take advantage from currently available large-data technologies, it is very likely that several words of caution should be observed. On the one hand, many (not all) the works rely on existing ECG databases, many of them available to the public thanks to the work made by the ECG signal processing community during the last several years [181,200,203]. However, the apparent diversity and large amount of data in terms of available beats in these data could be not enough for driving the training of DL based systems, due to points like the number of patients in these databases (several thousands of beats per patient is not necessarily a sign of large training sets, due to the intra-patient redundancy), or the unbalance among the actually needed classes in the clinical setting. Second, the existing works rarely pay attention to the acquisition and especially to these preprocessing tuning on the final classification systems.