Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals

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

doi:10.1109/globalsip.2016.7906025

Cited by 18 publications

(4 citation statements)

References 8 publications

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“…Furthermore, when examining patients in a stationary position, an error margin of 5 BPM [24] has been obtained. In contrast, when concentrating on activities that entail significant movement, such as running, the methods proposed in the literature yielded errors in the range of 11.47 BPM [20,21,23]. It is crucial to highlight that the existing literature does not present any algorithms explicitly designed for HR estimation during routine daily activities, which could be of considerable interest in monitoring patients or children with ASD.…”

Section: Discussionmentioning

confidence: 99%

“…ML can outperform adaptive filtering techniques due to its ability to learn autonomously, its ability to generalize to previously unseen data, its possibility to handle complex and non-linear features relationships, its flexibility in feature selection, and its scalability for large volumes of data. The work presented in [23] proposes the use of supervized learning using neural networks to face the problem without the need to use acceleration signals. Different peaks within the frequency spectrum of the raw PPG signal are selected, and a probability that each peak corresponds to the HR peak is assigned.…”

Section: Introductionmentioning

confidence: 99%

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An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts

et al. 2023

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The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts

et al. 2023

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“…(d) MA Removal Using Other Advanced Techniques. Machine learning approaches have been demonstrated as powerful techniques for MA removal [136]. The support vector machine was applied to identify high confident heartbeats from MA corrupted ECG signal in wearable applications [129].…”

Section: Uncertainties In Ppg Measurementmentioning

confidence: 99%

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Lü

Zhu

Morelli

et al. 2024

IEEE Rev. Biomed. Eng.

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Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

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“…To improve the performance, the network was additionally trained on the ADI dataset with the pre-trained model from the ISPC dataset, but the AAE was still high as 4.1 bpm. In [25], a simple fully connected layer was used for the model, where an acceleration signal was not also considered. For an input layer, 17 features from power spectrum were used.…”

Section: Performance Comparison With Additional Deep Learning Apprmentioning

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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Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals

Cited by 18 publications

References 8 publications

An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts

An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

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

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