ECG biometric authentication based on non-fiducial approach using kernel methods

Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)

Personal identity verification based ECG biometric using non-fiducial features

Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision

Shallow Neural Network for Biometrics from the ECG-WATCH

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate

Hybrid machine learning model based on feature decomposition and entropy optimization for higher accuracy flood forecasting

The advancement of machine learning model has widely been adopted to provide flood forecast. However, the model must deal with the challenges to determine the most important features to be used in in flood forecast with high-dimensional non-linear time series when involving data from various stations. Decomposition of time-series data such as empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform are widely used for optimization of input; however, they have been done for single dimension time-series data which are unable to determine relationships between data in high dimensional time series.  In this study, hybrid machine learning models are developed based on this feature decomposition to forecast the monthly water level using monthly rainfall data. Rainfall data from eight stations in Kelantan River Basin are used in the hybrid model. To effectively select the best rainfall data from the multi-stations that provide higher accuracy, these rainfall data are analyzed with entropy called Mutual Information that measure the uncertainty of random variables from various stations. Mutual Information act as optimization method helps the researcher to select the appropriate features to score higher accuracy of the model. The experimental evaluations proved that the hybrid machine learning model based on the feature decomposition and ranked by Mutual Information can increase the accuracy of water level forecasting.  This outcome will help the authorities in managing the risk of flood and helping people in the evacuation process as an early warning can be assigned and disseminate to the citizen

Hybrid machine learning model based on feature decomposition and entropy optimization for higher accuracy flood forecasting

The advancement of machine learning model has widely been adopted to provide flood forecast. However, the model must deal with the challenges to determine the most important features to be used in in flood forecast with high-dimensional non-linear time series when involving data from various stations. Decomposition of time-series data such as empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform are widely used for optimization of input; however, they have been done for single dimension time-series data which are unable to determine relationships between data in high dimensional time series.  In this study, hybrid machine learning models are developed based on this feature decomposition to forecast the monthly water level using monthly rainfall data. Rainfall data from eight stations in Kelantan River Basin are used in the hybrid model. To effectively select the best rainfall data from the multi-stations that provide higher accuracy, these rainfall data are analyzed with entropy called Mutual Information that measure the uncertainty of random variables from various stations. Mutual Information act as optimization method helps the researcher to select the appropriate features to score higher accuracy of the model. The experimental evaluations proved that the hybrid machine learning model based on the feature decomposition and ranked by Mutual Information can increase the accuracy of water level forecasting.  This outcome will help the authorities in managing the risk of flood and helping people in the evacuation process as an early warning can be assigned and disseminate to the citizen

Encryption by Heart (EbH)-Using ECG for time-invariant symmetric key generation

Wearable devices are a part of Internet-of-Things (IoT) that may offer valuable data of their porting user. This paper explores the use of ElectroCardioGram (ECG) records to encrypt user data. Previous attempts have shown that ECG can be taken as a basis for key generation. However, these approaches do not consider time-invariant keys. This feature enables using these so-created keys for symmetrically encrypting data (e.g. smartphone pictures), enabling their decryption using the key derived from the current ECG readings. This paper addresses this challenge by proposing EbH, a mechanism for persistent key generation based on ECG. EbH produces seeds from which encryption keys are generated. Experimental results over 24 h for 199 users show that EbH, under certain settings, can produce permanent seeds (thus time-invariant keys) computed on-the-fly and different for each user up to 95.97% of users produce unique keys. In addition, EbH can be tuned to produce seeds of different length (up to 300 bits) and with variable min-entropy (up to 93.51). All this supports the workability of EbH in a real setting. (C) 2017 Elsevier B.V. All rights reserved.Funding: This work was supported by the MINECO grants TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and TIN2016-79095-C2-2-R (SMOG-DEV); by the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks), which is co-funded by European Funds (FEDER); and by the Programa de Ayudas para la Movilidad of Carlos III University of Madrid, Spain (J. M. de Fuentes and L. Gonzalez-Manzano grants). Data used for this research was provided by the Telemetric and ECG Warehouse (THEW) of University of Rochester, NY

Privacy-Preserving ECG Based Active Authentication (PPEA2) Scheme for Iot Devices

Internet of things (IoT) devices are becoming ubiquitous in, and even essential to, many aspects of day-to-day life, from fitness trackers, pacemakers, to industrial control systems. On a larger scale, live stream of sleep patterns data recorded via fitness tracker devices was utilized to quantify the effect of a seismic activity on sleep. While the benefits of IoT are undeniable, IoT ecosystem comes with its own set of system vulnerabilities that include malicious actors manipulating the flow of information to and from the IoT devices, which can lead to the capture of sensitive data and loss of data privacy. My thesis explores a Privacy-Preserving ECG based Active Authentication (PPEA2) scheme that is deployable on power-limited wearable systems to counter these vulnerabilities. Electrocardiogram (ECG) is a record of the electrical activity of the heart, and it has been shown to be unique for every person. This work leverages that idea to design a feature extraction followed by an authentication scheme based on the extracted features. The proposed scheme preserves the privacy of the extracted features by employing a light-weight secure computation approach based on secure weighted hamming distance computation from an oblivious transfer. It computes a joint set between two participating entities without revealing the keys to either of them

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz