# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
## 1 Introduction  
At present, we are experiencing emerging digitization in most aspects of our lives. Day-to-day, use online applications and services such as mobile banking, social networking, online stock exchange, and trading or email services, with little apprehension about storing personal confidential information on our devices or client servers. In the digital era, sadly, we are also faced with new attacks and exploits, as well as unauthorized access to sensitive information and devices by malicious viruses or hosts. It is increedible that large populations of users still rely on numerous types or particular sets of passwords, which have been used for authorized access since the earliest era of computing. In recent years, there has been a shift in attention towards biometric security systems. These security applications facilitate the identification of an individual using their distinct biological characteristics instead of a set of numerical or alphabetical passwords. The most widespread techniques use fingerprint, iris, and facial recognition approaches, and are normally found in smart devices [1]. Regarding biometrics in mobile devices, the benefits are apparent. There is an added degree of security in relying, at least in part, on an extremity (e.g., finger) that only the userpossesses. For instance, with a standard password, together with a personal fingerprint, the sense of security is enhanced. Beyond smartphone security, the use of features such as touch IDs improves the convenience of interacting with your device. With applications based on such security systems, instead of manually entering your payment information, users simply have to swipe a finger across their device. It is simple and saves time. Using biometric authentication for security purposes also improves the security of information, processes, and establishments. Some organizations have implemented biometric scanning as a modern method of \"punching in\" to work. This assures that all employees are honest in terms of the hours they have worked. In turn, this saves the organization money. However, there are still difficulties and issues related to fingerprint usability and reliability. Current challenges in ECG biometric classification [2; 3; 4] tasks include the extraction of features from the ECG signals to implement a model to learn hidden patterns for accurate generalization, proving the stability of the biometric and protecting against attacks. In this report, we proposed biometric-based ECG signals for human authentication with non-fiducial techniques. The ECG signal usually consists of three complexes, namely P, QRS, and T. They are determined based on their corresponding complexes, also known as fiducial points, which are the peak points of the respective complex. Using this information as distinct features, more informative characteristics including time-domain features such as amplitudes and intervals, are generally used as features for individual signals. However, the shapes of the ECG signal can vary depending on the location of the electrode on the human body during data acquisition. P-QRS-T complexes are not available for every version of the ECG signal. Thus, a non-fiducial approach using machine learning is used to address these problems. By introducing a non-fiducial method based on deep learning techniques, the fiducial extraction can be neglected in the pre-processing phase. In addition, unlike previous studies that relied on specified data for classification, a data-independent acquisition technique is also extended to form a model that can handle various types of ECG data input. To enable a data-independent and highly generalizable signal processing and feature learning process, a random segmentation based a wavelet domain multiresolution bi-directional LSTM network is proposed. Specifically, it allows for blindly selecting a physiological signal segment for classification purpose, avoiding the complicated signal fiducial characteristics extraction process which does not rely on QRS peak detection.