ECG characteristic points detection using general regression neural network-based particle filters

Li, Guojun; Zhou, Xiaona; Zhang, Shuting; Liu, Nai-Qian

doi:10.1109/isbb.2011.6107669

Cited by 3 publications

References 8 publications

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A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts

Nathan

Akkaya

Jafari

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this work, we describe a methodology to probabilistically estimate the R-peak locations of an electrocardiogram (ECG) signal using a particle filter. This is useful for heart rate estimation, which is an important metric for medical diagnostics. Some scenarios require constant in-home monitoring using a wearable device. This poses a particularly challenging environment for heart rate detection, due to the susceptibility of ECG signals to motion artifacts. In this work, we show how the particle filter can effectively track the true R-peak locations amidst the motion artifacts, given appropriate heart rate and R-peak observation models. A particle filter based framework has several advantages due to its freedom from strict assumptions on signal and noise models, as well as its ability to simultaneously track multiple possible heart rate hypotheses. Moreover, the proposed framework is not exclusive to ECG signals and could easily be leveraged for tracking other physiological parameters. We describe the implementation of the particle filter and validate our approach on real ECG data affected by motion artifacts from the MIT-BIH noise stress test database. The average heart rate estimation error is about 5 beats per minute for signal streams contaminated with noisy segments with SNR as low as -6 dB.

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A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts

Nathan

Akkaya

Jafari

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Particle Filtering and Sensor Fusion for Robust Heart Rate Monitoring Using Wearable Sensors

Nathan

Jafari

2018

IEEE J. Biomed. Health Inform.

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This paper describes a novel methodology leveraging particle filters for the application of robust heart rate monitoring in the presence of motion artifacts. Motion is a key source of noise that confounds traditional heart rate estimation algorithms for wearable sensors due to the introduction of spurious artifacts in the signals. In contrast to previous particle filtering approaches, we formulate the heart rate itself as the only state to be estimated, and do not rely on multiple specific signal features. Instead, we design observation mechanisms to leverage the known steady, consistent nature of heart rate variations to meet the objective of continuous monitoring of heart rate using wearable sensors. Furthermore, this independence from specific signal features also allows us to fuse information from multiple sensors and signal modalities to further improve estimation accuracy. The signal processing methods described in this work were tested on real motion artifact affected electrocardiogram and photoplethysmogram data with concurrent accelerometer readings. Results show promising average error rates less than 2 beats/min for data collected during intense running activities. Furthermore, a comparison with contemporary signal processing techniques for the same objective shows how the proposed implementation is also computationally more efficient for comparable performance.

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A Spline Framework for Optimal Representation of Semiperiodic Signals

Guilak¹

2000

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Semiperiodic signals possess an underlying periodicity, but their constituent spectral components include stochastic elements which make it impossible to analytically determine locations of the signal's critical points. Mathematically, a signal's critical points are those at which it is not differentiable or where its derivative is zero. In some domains they represent characteristic points, which are locations indicating important changes in the underlying process reflected by the signal.For many applications in healthcare, knowledge of precise locations of these points provides key insight for analytic, diagnostic, and therapeutic purposes. For example, given an appropriate signal they might indicate the start or end of a breath, numerous electrophysiological states of the heart during the cardiac cycle, or the point in a stride at which the heel impacts the ground. The inherent variability of these signals, the presence of noise, and often, very low signal amplitudes, makes accurate estimation of these points challenging.There has been much effort in automatically estimating characteristic point locations. Approaches include algorithms operating in the time domain, on various transformations of the data, and using different models of the signal. These methods apply a wide variety of techniques ranging from simple thresholds and search windows to sophisticated signal processing and pattern recognition algo-

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ECG characteristic points detection using general regression neural network-based particle filters

Cited by 3 publications

References 8 publications

A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts

A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts

Particle Filtering and Sensor Fusion for Robust Heart Rate Monitoring Using Wearable Sensors

A Spline Framework for Optimal Representation of Semiperiodic Signals

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