Analytical Approach to Biometric Security and How It Affects Privacy

In this time where the world is using technology every day, there is going to be a need for some type of security to take place to protect its citizens from unwanted harm or danger. The use of any authentication methods is becoming very essential for a lot of companies and even for your own personal belongings. The use of biometric technology has offered companies the chance to upgrade their security system. This has also provided easier ways that people authenticate themselves as who they say they are. Due to their growth of usage, there is a privacy and security concern of these biometric data. In this research, we developed an analytical approach to biometric security in relation to privacy. This research will focus on the history, current state, problems/concerns, and the future development of biometric security. Biometric will be a forever growing topic and forever changing as time goes by. There will be a future in how companies will be using biometric technologies to better secure their systems

Effects of lead position, cardiac rhythm variation and drug-induced QT prolongation on performance of machine learning methods for ECG processing

Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effects of QT-prolongation agents on the performance of machine learning methods for ECG processing. We choose the problem of subject identification for analysis, because this problem may be solved for almost any available dataset of ECG data. In a discussion, we compare our findings with observations from other works that use machine learning for ECG processing with different problem statements. Our results show the importance of training dataset enrichment with ECG signals acquired in specific physiological conditions for obtaining good performance of ECG processing for real applications

Implementasi Pengamanan Transmisi Sinyal EKG (Elektrokardiogram) secara Daring dengan Metode Anonimasi

ABSTRAK Data World Health Organization (WHO) pada tahun 2014 menunjukkan bahwa di Indonesia sebanyak 37% dari seluruh penyebab kematian adalah penyakit yang berhubungan dengan jantung. Kehadiran teknologi dan pemanfaatan Internet of Things (IoT) diharapkan dapat membantu mengurangi resiko kematian akibat penyakit jantung tersebut. Pada penelitian ini, pengukuran dan pengamatan sinyal jantung melalui tele-auskultasi sinyal elektrokardiogram (EKG) dilakukan. Untuk mengamankan sinyal EKG dalam proses transmisi melalui jaringan Internet digunakan metode anonimasi sinyal berbasis algoritma Jusak-Seedahmed. Hasil pengujian menunjukkkan bahwa algoritma Jusak-Seedahmed dapat melakukan proses anonimasi dan proses rekonstruksi sinyal dengan baik. Pengujian korelasi silang antara sinyal hasil rekonstruksi dan sinyal EKG asli sebelum anonimasi menghasilkan korelasi sebesar 1 pada lag=0. Sinyal EKG hasil rekonstruksi ditampilkan dalam aplikasi mobile untuk memudahkan analisis oleh dokter. Kata kunci: elektrokardiogram, keamanan, anonimasi, IoT, FFT   ABSTRACT Based on the latest data released by the World Health Organization in 2014, deaths caused by cardiovascular disease in 2012 have reached 37% of the total number of non-communicable diseases deaths in Indonesia. Therefore, it is expected that the applications of the Internet of Things (IoT) might be used to reduce the risk of death due to the heart related problems. In this research, a tele-auscultation technique for measuring and monitoring electrocardiogram (ECG) signal was built. To secure transmission of the ECG signal over the Internet, we implemented a recently proposed Jusak-Seedahmed algorithm. Our examinations showed that the algorithm performed the anonymization and reconstruction processes well. Crosscorrelation analysis showed that correlation between the reconstructed and the original ECG signal at lag=0 was 1. Furthermore, a mobile-based application had been built to display the reconstructed ECG signal for further analysis. Keywords: electrocardiogram, security, anonimization, IoT, FF

Implementasi Pengamanan Transmisi Sinyal EKG (Elektrokardiogram) secara Daring dengan Metode Anonimasi

Data World Health Organization (WHO) pada tahun 2014 menunjukkan bahwa di Indonesia sebanyak 37% dari seluruh penyebab kematian adalah penyakit yang berhubungan dengan jantung. Kehadiran teknologi dan pemanfaatan Internet of Things (IoT) diharapkan dapat membantu mengurangi resiko kematian akibat penyakit jantung tersebut. Pada penelitian ini, pengukuran dan pengamatan sinyal jantung melalui tele-auskultasi sinyal lektrokardiogram (EKG) dilakukan. Untuk mengamankan sinyal EKG dalam proses transmisi melalui jaringan Internet digunakan metode anonimasi sinyal berbasis algoritma Jusak-Seedahmed. Hasil pengujian menunjukkkan bahwa algoritma Jusak-Seedahmed dapat melakukan proses anonimasi dan proses rekonstruksi sinyal dengan baik. Pengujian korelasi silang antara sinyal hasil rekonstruksi dan sinyal EKG asli sebelum anonimasi menghasilkan korelasi sebesar 1 pada lag=0. Sinyal EKG hasil rekonstruksi ditampilkan dalam aplikasi mobile untuk memudahkan analisis oleh dokter

An enhanced machine learning-based biometric authentication system using RR- Interval Framed Electrocardiograms

This paper is targeted in the area of biometric data enabled security by using machine learning for the digital health. The traditional authentication systems are vulnerable to the risks of forgetfulness, loss, and theft. Biometric authentication is has been improved and become the part of daily life. The Electrocardiogram (ECG) based authentication method has been introduced as a biometric security system suitable to check the identification for entering a building and this research provides for studying ECG-based biometric authentication techniques to reshape input data by slicing based on the RR-interval. The Overall Performance (OP) as a newly proposed performance measure is the combined performance metric of multiple authentication measures in this study. The performance of the proposed system using a confusion matrix has been evaluated and it has achieved up to 95% accuracy by compact data analysis. The Amang ECG (amgecg) toolbox in MATLAB is applied to the mean square error (MSE) based upper-range control limit (UCL) which directly affects three authentication performance metrics: the number of accepted samples, the accuracy and the OP. Based on this approach, it is found that the OP could be maximized by applying a UCL of 0.0028, which indicates 61 accepted samples within 70 samples and ensures that the proposed authentication system achieves 95% accuracy

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems