As the Covid-19 pandemic becomes a nationwide problem, physical contact is no longer acceptable. Therefore, biometric technology can be used for practicing social distancing to prevent the spread of the virus. However, face and fingerprint are vulnerable to presentation attacks. Hence alternative modalities such as ECG based biometric become popular. In this paper, we develop a novel presentation attack using a GAN where a short template of the victim's ECG is captured by an attacker and used to generate synthetic fake ECG signals. We also propose a novel framework utilizing residual neural network architecture to analyze ECG presentation attacks.
more »
« less
Trustworthy of Implantable Medical Devices using ECG Biometric
Implantable medical devices (IMD) such as pacemakers, and cardiac defibrillators are becoming increasingly interconnected to networks for remote patient monitoring. However, networked devices are vulnerable to external attacks that could allow adversaries to gain unauthorized access to devices/ data and break patient privacy. To design a lightweight computational trustworthy of IMD, we propose novel ECG-based biometric authentication using lift and shift method based on post-processing data from the noise generated in an ECG signal recording. The lift and shift method is an ideal addition to this system because it is a quick, lightweight process that produces enough random bits for encrypted communication. ECG is a signal that is already being measured by the IMD, so this ECG biometric could utilize the data that is already being actively recorded. We provide a comprehensive evaluation across multiple NIST tests, as well as ENT and Dieharder statistical suites test
more »
« less
- Award ID(s):
- 2302084
- NSF-PAR ID:
- 10514648
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2157-9
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- San Jose, CA, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.more » « less
-
Electrocardiography (ECG) is the process of recording the electrical activity of the human heart over time using electrodes that are placed over the skin. While the primary usage of electrocardiograms, the recorded signals, has been focused on the check of signs of heart-related diseases, recent studies have moved also toward their usage for human authentication. Thus, an ECG signal can be unique enough to be used independently as a biometric modality. In addition to its inherent liveness detection, it is easy to collect and can be easily captured either via sensors attached to the human body (fingertips, chest, wrist) or even passively using wireless sensors. In this paper, we propose a novel approach that exploits the spectro-temporal dynamic characteristics of the ECG signal to establish personal recognition system using both short-time Fourier transform (STFT) and generalized Morse wavelets (CWT). This process results in enriching the information extracted from the original ECG signal that is inserted in a 2D convolutional neural network (CNN) which extracts higher level and subject-specific ECG-based features for each individual. To validate our proposed CNN model, we performed nested cross-validation using eight different ECG databases. These databases are considered challenging since they include both normal and abnormal heartbeats as well as a dynamic number of subjects. Our proposed algorithms yield superior performance when compared to other state-ofart approaches discussed in the literature, i.e. the STFT-based one achieves an average identification rate, equal error rate (EER), and area under curve (AUC) of 97.86%, 0.0268, and 0.9933 respectively, whereas the CWT achieves comparable to STFT results in 97.5%, 0.0386, and 0.9882 respectively.more » « less
-
null (Ed.)The growing demand for recording longer ECG signals to improve the effectiveness of IoT-enabled remote clinical healthcare is contributing large amounts of ECG data. While lossy compression techniques have shown potential in significantly lowering the amount of data, investigation on how to trade-off between data reduction and data fidelity on ECG data received relatively less attention. This paper gives insight into the power of lossy compression to ECG signals by balancing between data quality and compression ratio. We evaluate the performance of transformed-based lossy compressions on the ECG datasets collected from the Biosemi ActiveTwo devices. Our experimental results indicate that ECG data exhibit high energy compaction property through transformations like DCT and DWT, thus could improve compression ratios significantly without hurting data fidelity much. More importantly, we evaluate the effect of lossy compression on ECG signals by validating the R-peak in the QRS complex. Our method can obtain low error rates measured in PRD (as low as 0.3) and PSNR (up to 67) using only 5% of the transform coefficients. Therefore, R-peaks in the reconstructed ECG signals are almost identical to ones in the original signals, thus facilitating extended ECG monitoring.more » « less
-
null (Ed.)Background Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Results Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. Conclusions iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. Trial Registration UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284 International Registered Report Identifier (IRRID) DERR1-10.2196/24799more » « less