# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 6 Applications in ECG Processing  
It has been proven that some medical conditions, like diabetes and hypertension, and some risk factors, like smoking, can affect the state of the heart [177]. However, unhealthy or just disordered lifestyles nowadays certainly increase the risk of suffering heart diseases. The most important heart affliction, and the leading cause of death worldwide according World Health Organization [178], is the ischemic heart disease, which occurs when one or several coronary arteries are blocked, which causes nutrition loss of myocardial cells and result in a myocardial infarction or heart attack. The consequences of this affliction can be as serious as death, hence diagnosing and treating it as quickly as possible gives a better scope for survival and quality of life. Another important set of heart diseases are the cardiac arrhythmias. These disorders produce a modification of the heart beat variation, either making it slower (bradicardia) or faster (tachycardia), which also can be a cause of patient condition deterioration and exits in some circumstances. There are many other pathological heart disorders, like congenital heart diseases, valvular heart diseases, infections, inflammations, and many others [177]. On the other hand, physiological signals are electrical signals which come from sensors placed on the body skin, and whose characteristics are determined by the sensor location. These signals are an invaluable data source which can be used to disease detection [50]. One of these physiological signals is the ECG, which is generated from the contraction and the recovery of the heart [53], and it is extensively used for diagnosing several heart diseases and conditions, like myocardial infarction, arrhythmias, hypertrophic cardiomyopathy, or dilated cardiomyopathy, among many others [177].  
With the purpose of helping with the diagnosis of cardiac diseases, relevant information must be extracted from the ECG by the interpretation of the physicians. This human interpretation demands years of training in order to acquire a specialized knowledge. However, even with expert knowledge, these human interpretations undergo some problems such as intra- and inter-operation variability, or errors by fatigue due to the tedious process of analyzing the ECG. With all this, it has been suggested to use computer-supported signal analysis systems, which can avoid intra- and inter-operation variability and errors by fatigue [50]. One should keep in mind that the computer supported ECG signal analysis is not a new necessity, and it has been pursued for several decades. Until recently, the process was to apply some low-level signal processing for noise reduction, extract hand-crafted features and use a machine learning or a statistical classifiers. However, these kinds of solutions are not effective for high-dimensional, more complex, real-world noisy signals, and these systems can suffer from an unreliable accuracy [22]. DL has been suggested to likely achieve a more effective analysis of ECG signals because its proven significant and remarkable improvements in robustness to noise and variability in several pattern recognition applications help real-time classification of very complex signals, though this scenario also raises new challenges, like regarding data availability [53; 179]. Apart from health monitoring and medical diagnosis, the use of ECG as a biometric trait for identification or authentication has gained momentum [180]. The ECG, compared with other biometric traits, has proven to be one of the most promising among them, and researchers have recently also started to use DL methodologies in this area, which is today a pioneering affair [53].