ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

Yamamoto, Kohei; Hiromatsu, Ryosuke; Ohtsuki, Tomoaki

doi:10.1109/access.2020.3009266

Cited by 53 publications

(18 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, radar information for reconstructing the ground truth PPG signal is also a possible threat. Yamamoto et al [145] explored the potential to reconstruct the ECG signal using the information collected by the doppler sensor. However, no researcher has used it for the reconstruction of PPG signals.…”

Section: Attack Threatsmentioning

confidence: 99%

SoK: An Overview of PPG's Application in Authentication

Li¹,

Chen²,

Pan³

et al. 2022

Preprint

View full text Add to dashboard Cite

Biometric authentication prospered during the 2010s. Vulnerability to spoofing attacks remains an inherent problem with traditional biometrics. Recently, unobservable physiological signals (e.g., Electroencephalography, Photoplethysmography, Electrocardiography) as biometrics have been considered a potential solution to this problem. In particular, Photoplethysmography (PPG) measures the change of blood flow of the human body by an optical method. Clinically, researchers commonly use PPG signals to obtain patients' blood oxygen saturation, heart rate, and other information to assist in diagnosing heart-related diseases. Since PPG signals are easy to obtain and contain a wealth of individual cardiac information, researchers have begun to explore its potential applications in information security. The unique advantages (simple acquisition, difficult to steal, and live detection) of the PPG signal allow it to improve the security and usability of the authentication in various aspects. However, the research on PPG-based authentication is still in its infancy. The lack of systematization hinders new research in this field. We conduct a comprehensive study of PPGbased authentication and discuss these applications' limitations before pointing out future research directions.

show abstract

Section: Attack Threatsmentioning

confidence: 99%

SoK: An Overview of PPG's Application in Authentication

Li¹,

Chen²,

Pan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Vital-sign-sensing methods using radars [6]- [9] and optical cameras [10]- [12] to achieve contactless heartbeat monitoring have already been investigated. In addition to their ability to acquire the displacement signals from subjects without contact, radar methods can protect privacy and measure signals regardless of ambient brightness [13], [14].…”

Section: Introductionmentioning

confidence: 99%

ECG Signal Reconstruction Using FMCW Radar and a Convolutional Neural Network for Contactless Vital-Sign Sensing

Anzai

Ichige

Saito

et al. 2023

IEICE Trans. Commun.

View full text Add to dashboard Cite

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring bodysurface-displacement signals by using frequency-modulated continuouswave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

show abstract

“…Currently, neural network approaches for signal reconstruction are used by Yamamoto et al [ 13 ]. This group reconstructed an ECG with Doppler sensor data.…”

Section: Introductionmentioning

confidence: 99%

GRU Neural Network Improved Bioimpedance Based Stroke Volume Estimation during Ergometry Stress Test

Urban

Klum

Pielmuş

et al. 2022

Sensors

View full text Add to dashboard Cite

Cardiovascular diseases (CVDs) are one of the leading members of non-communicable diseases. An early diagnosis is essential for effective treatment, to reduce hospitalization time and health care costs. Nowadays, an exercise stress test on an ergometer is used to identify CVDs. To improve the accuracy of diagnostics, the hemodynamic status and parameters of a person can be investigated. For hemodynamic management, thoracic electrical bioimpedance has recently been used. This technique offers beat-to-beat stroke volume calculation but suffers from an artifact-sensitive signal that makes such measurements difficult during movement. We propose a new method based on a gated recurrent unit (GRU) neural network and the ECG signal to improve the measurement of bioimpedance signals, reduce artifacts and calculate hemodynamic parameters. We conducted a study with 23 subjects. The new approach is compared to ensemble averaging, scaled Fourier linear combiner, adaptive filter, and simple neural networks. The GRU neural network performs better with single artifact events than shallow neural networks (mean error −0.0244, mean square error 0.0181 for normalized stroke volume). The GRU network is superior to other algorithms using time-correlated data for the exercise stress test.

show abstract

ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

Cited by 53 publications

References 46 publications

SoK: An Overview of PPG's Application in Authentication

SoK: An Overview of PPG's Application in Authentication

ECG Signal Reconstruction Using FMCW Radar and a Convolutional Neural Network for Contactless Vital-Sign Sensing

GRU Neural Network Improved Bioimpedance Based Stroke Volume Estimation during Ergometry Stress Test

Contact Info

Product

Resources

About