An ECG dataset representing real-world signal characteristics for wearable computers

Zhang, Qingxue; Zahed, Chakameh; Nathan, Viswam; Hall, Drew A.; Jafari, Roozbeh

doi:10.1109/biocas.2015.7348384

Cited by 11 publications

(1 citation statement)

References 5 publications

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“…This section shows the simulation results for each AFE model, which demonstrate their behavior when applied to real-world ECG signals. ECG signals are from a large dataset provided by [17], the ECG signals are acquired from multiple healthy individuals having various activities, obtained from different parts of the human body (wrist and chest) and with different acquisition forms, ac and dc coupling, patch or dry electrodes and different common mode rejection methods. Moreover, the samples comprised in the dataset have influence from the major types of interference associated with the ECG signal, BLW, MA and powerline interference.…”

Section: Architectural Simulation Resultsmentioning

confidence: 99%

Behavioral Analysis of Noise and Bandwidth Specifications of Heartbeat Detection Circuits for Ultra Low Power Devices

et al. 2023

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This paper presents the behavioral modeling of three circuit architectures, which are suitable for heartbeat detection in wireless healthcare applications. The developed front-end models implement an analog system that detects the heartbeats for lower power consumption and higher detection accuracy without digital signal processing. Therefore, the circuit blocks and their critical specifications specifically regarding the noise and bandwidth are evaluated. The architectures are implemented in Verilog-A and their behavior simulations use real electrocardiogram (ECG) signals with different characteristics and conditions. All architectures have amplification, band-pass and notch response blocks as their first three stages. Then, the first circuit architecture detects the heartbeat using an adaptive threshold based on a pulse width approach. The second heartbeat detection scheme finds the maximum and minimum values of each beat using a sample-and-hold-based peak detector. The third model uses a similar peak detection scheme, but delays the analog signal for better precision. The simulation results show that all models can detect heartbeats when the input noise is less than 20 µV , the third having the highest noise tolerance of around 50 µV . For the ECG signal to maintain its characteristics with respect to its positive and negative peak, the bandpass response should present a low cutoff frequency below 10 Hz.

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Section: Architectural Simulation Resultsmentioning

confidence: 99%

Behavioral Analysis of Noise and Bandwidth Specifications of Heartbeat Detection Circuits for Ultra Low Power Devices

et al. 2023

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Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification

Zhang

Zhou

2017

Ann Biomed Eng

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In this study, to advance smart health applications which have increasing security/privacy requirements, we propose a novel highly wearable ECG-based user identification system, empowered by both non-standard convenient ECG lead configurations and deep learning techniques. Specifically, to achieve a super wearability, we suggest situating all the ECG electrodes on the left upper-arm, or behind the ears, and successfully obtain weak but distinguishable ECG waveforms. Afterwards, to identify individuals from weak ECG, we further present a two-stage framework, including ECG imaging and deep feature learning/identification. In the former stage, the ECG heartbeats are projected to a 2D state space, to reveal heartbeats' trajectory behaviors and produce 2D images by a split-then-hit method. In the second stage, a convolutional neural network is introduced to automatically learn the intricate patterns directly from the ECG image representations without heavy feature engineering, and then perform user identification. Experimental results on two acquired datasets using our wearable prototype, show a promising identification rate of 98.4% (single-arm-ECG) and 91.1% (ear-ECG), respectively. To the best of our knowledge, it is the first study on the feasibility of using single-arm-ECG/ear-ECG for user identification purpose, which is expected to contribute to pervasive ECG-based user identification in smart health applications.

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SMHeart ID: smart ID based on ECG and long short term memory for remote health monitoring

Saxena,

Patel

2024

Multimed Tools Appl

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An ECG dataset representing real-world signal characteristics for wearable computers

Cited by 11 publications

References 5 publications

Behavioral Analysis of Noise and Bandwidth Specifications of Heartbeat Detection Circuits for Ultra Low Power Devices

Behavioral Analysis of Noise and Bandwidth Specifications of Heartbeat Detection Circuits for Ultra Low Power Devices

Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification

SMHeart ID: smart ID based on ECG and long short term memory for remote health monitoring

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