Low Complexity Heart Rate Measurement from Wearable Wrist-Type Photoplethysmographic Sensors Robust to Motion Artifacts

Mashhadi, Mahdi Boloursaz; Farhadi, Majid; Essalat, Mahmoud; Marvasti, Farokh

doi:10.1109/icassp.2018.8461520

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…Nowak et al [73] had applied non-negative matrix factorization before applying subtraction, and Sun et al [74] introduced a new methodology named as Grid-less Spectral Estimation (GRESS) which is basically a sparsity-based method for spectral estimation. Mashhadi et al [75] introduced a novel method of spectral division in which PPG spectra are divided by ACC spectra. The author had opinion that this method is useful as it automatically scales PPG spectra and has linear time complexity.…”

Section: Other Techniquesmentioning

confidence: 99%

“…Pan et al [89] employed HR modeling along with heuristics to reduce the error. Mashhadi et al [75], instead of using heuristics, employed lazy tracker which was basically an averaging tracker. The strongest motivation in the development of signal processing-based tracker is to develop such a tracker which has low computational cost and as such can be used for realtime processing.…”

Section: Sptmentioning

confidence: 99%

See 1 more Smart Citation

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Ismail

Akram

Siddiqi

2021

EURASIP J. Adv. Signal Process.

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Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

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Section: Other Techniquesmentioning

confidence: 99%

Section: Sptmentioning

confidence: 99%

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Ismail

Akram

Siddiqi

2021

EURASIP J. Adv. Signal Process.

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“…In the proposed model, we also present a new scheme to evaluate the loss value by modifying the true HR value into a Gaussian distribution. The performance of the proposed model is evaluated by comparison with previously reported results [1]- [17], [22].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Heart Rate Estimation From Reflectance Photoplethysmography With Acceleration Power Spectrum and Acceleration Intensity

Chung

Lee

et al. 2020

IEEE Access

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A wearable reflectance-type photoplethysmography (PPG) sensor can be incorporated in a watch or band to provide instantaneous heart rates (HRs) with minimum inconvenience to users. However, the sensor is sensitive to motion artifacts (MAs), which results in inaccurate HR estimation. To address this problem, we propose a new neural network for deep learning to ensure accurate HR estimation even during intensive exercise. Methods: We propose a new deep neural network based on multiclass and non-uniform multilabel classification for HR estimation. It comprises of two convolutional layers, two long short-term memory (LSTM) layers, one concatenation layer, and three fully connected layers including a softmax. The proposed model feeds the power spectra from the PPG and acceleration signals along with the acceleration intensity to the input layer. We also present a new scheme to evaluate the loss value by modifying the true HR value into a Gaussian distribution. Results: We used 48 training datasets and evaluated 23 isolated testing datasets. The proposed model exhibited average absolute error of less than 1.5 bpm for all the training and test datasets-1.09 bpm for the training dataset and 1.46 bpm for the test dataset. Conclusion: The proposed model outperforms the state-of-the-art methods for accurate estimation of HR. Significance: It precisely estimates the HRs with robustness even during intensive physical exercise, as evidenced by the accuracy when PPG signals are severely corrupted by MAs. INDEX TERMS Reflectance-type photoplethysmography, instantaneous heart rate, deep learning, convolutional layer, long short-term memory (LSTM) layer.

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A novel and low-complexity peak detection algorithm for heart rate estimation from low-amplitude photoplethysmographic (PPG) signals

Prada

Maldonado

2018

Journal of Medical Engineering & Technology

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Low Complexity Heart Rate Measurement from Wearable Wrist-Type Photoplethysmographic Sensors Robust to Motion Artifacts

Cited by 11 publications

References 17 publications

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Deep Learning for Heart Rate Estimation From Reflectance Photoplethysmography With Acceleration Power Spectrum and Acceleration Intensity

A novel and low-complexity peak detection algorithm for heart rate estimation from low-amplitude photoplethysmographic (PPG) signals

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