BAED: A secured biometric authentication system using ECG signal based on deep learning techniques

Prakash, Allam Jaya; Patro, Kiran Kumar; Hammad, Mohamed; Tadeusiewicz, Ryszard; Pławiak, Paweł

doi:10.1016/j.bbe.2022.08.004

Cited by 40 publications

(21 citation statements)

References 52 publications

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“…To verify the effectiveness of the proposed method in this paper, the mainstream image style transfer methods AdaIn [ 22 ], WCT [ 10 ], AAMS [ 25 ] and model stroke pyramid [ 24 ] are used for effect comparison. The purpose of this method is to keep the content expression of the subject as much as possible when dealing with shallow depth of field images so that it does not integrate with the texture generated by the background.…”

Section: Resultsmentioning

confidence: 99%

An improved defocusing adaptive style transfer method based on a stroke pyramid

et al. 2023

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Image style transfer aims to assign a specified artist’s style to a real image. However, most existing methods cannot generate textures of various thicknesses due to the rich semantic information of the input image. The image loses some semantic information through style transfer with a uniform stroke size. To address the above problems, we propose an improved multi-stroke defocus adaptive style transfer framework based on a stroke pyramid, which mainly fuses various stroke sizes in the image spatial dimension to enhance the image content interpretability. We expand the receptive field of each branch and then fuse the features generated by the multiple branches based on defocus degree. Finally, we add an additional loss term to enhance the structural features of the generated image. The proposed model is trained using the Common Objects in Context (COCO) and Synthetic Depth of Field (SYNDOF) datasets, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to evaluate the overall quality of the output image and its structural similarity with the content image, respectively. To validate the feasibility of the proposed algorithm, we compare the average PSNR and SSIM values of the output of the modified model and those of the original model. The experimental results show that the modified model improves the PSNR and SSIM values of the outputs by 1.43 and 0.12 on average, respectively. Compared with the single-stroke style transfer method, the framework proposed in this study improves the readability of the output images with more abundant visual expression.

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Section: Resultsmentioning

confidence: 99%

An improved defocusing adaptive style transfer method based on a stroke pyramid

et al. 2023

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“…A wavelet scattering filter bank is constructed giving the length of the signal, sampling frequency, quality factor of [ 8 , 1 ], and default invariance scale. We extracted the features from Fig 15 , using the function feature matrix, reducing down to a feature set of 499 by 8, which is a 95% reduction in the size of features.…”

Section: Resultsmentioning

confidence: 99%

“…Physiological signals such as the electrocardiogram (ECG) carry information on the human heart’s electrical activity, and its analysis helps in understanding the cardiovascular health of the patient [ 4 – 7 ]. Recently, a biometric authentication system has been developed based on a person’s ECG to make it more secure [ 8 ]. The recording is performed by conventionally attaching leads to the surface of the human body over specific areas.…”

Section: Introductionmentioning

confidence: 99%

Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals

Nawaz

Ahmed

2022

PLoS ONE

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Real-time data collection and pre-processing have enabled the recognition, realization, and prediction of diseases by extracting and analysing the important features of physiological data. In this research, an intelligent end-to-end system for anomaly detection and classification of raw, one-dimensional (1D) electrocardiogram (ECG) signals is given to assess cardiovascular activity automatically. The acquired raw ECG data is pre-processed carefully before storing it in the cloud, and then deeply analyzed for anomaly detection. A deep learning-based auto-encoder(AE) algorithm is applied for the anomaly detection of 1D ECG time-series signals. As a next step, the implemented system identifies it by a multi-label classification algorithm. To improve the classification accuracy and model robustness the improved feature-engineered parameters of the large and diverse datasets have been incorporated. The training has been done using the amazon web service (AWS) machine learning services and cloud-based storage for a unified solution. Multi-class classification of raw ECG signals is challenging due to a large number of possible label combinations and noise susceptibility. To overcome this problem, a performance comparison of a large set of machine algorithms in terms of classification accuracy is presented on an improved feature-engineered dataset. The proposed system reduces the raw signal size up to 95% using wavelet time scattering features to make it less compute-intensive. The results show that among several state-of-the-art techniques, the long short-term memory (LSTM) method has shown 100% classification accuracy, and an F1 score on the three-class test dataset. The ECG signal anomaly detection algorithm shows 98% accuracy using deep LSTM auto-encoders with a reconstructed error threshold of 0.02 in terms of absolute error loss. Our approach provides performance and predictive improvement with an average mean absolute error loss of 0.0072 for normal signals and 0.078 for anomalous signals.

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“…Independent research proves the authenticity of ECG signals [ 8 , 11 , 12 , 13 ]. Guglielmi et al [ 11 ] collected ECG signals from various sensor nodes on the individual’s body and used them to develop a new key agreement protocol that exploits the randomness of ECG signals.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard

Agrawal

Gebali

et al. 2023

Sensors

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Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

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BAED: A secured biometric authentication system using ECG signal based on deep learning techniques

Cited by 40 publications

References 52 publications

An improved defocusing adaptive style transfer method based on a stroke pyramid

An improved defocusing adaptive style transfer method based on a stroke pyramid

Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

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