research-article

PPG-based Heart Rate Estimation with Time-Frequency Spectra: A Deep Learning Approach

Authors:

Philip Schmidt,

Ina Indlekofer,

Kristof Van LaerhovenAuthors Info & Claims

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

Pages 1283 - 1292

https://doi.org/10.1145/3267305.3274176

Published: 08 October 2018 Publication History

Abstract

PPG-based continuous heart rate estimation is challenging due to the effects of physical activity. Recently, methods based on time-frequency spectra emerged to compensate motion artefacts. However, existing approaches are highly parametrised and optimised for specific scenarios. In this paper, we first argue that cross-validation schemes should be adapted to this topic, and show that the generalisation capabilities of current approaches are limited. We then introduce deep learning, specifically CNN-models, to this domain. We investigate different CNN-architectures (e.g. the number of convolutional layers, applying batch normalisation, or ensemble prediction), and report insights based on our systematic evaluation on two publicly available datasets. Finally, we show that our CNN-based approach performs comparably to classical methods.

References

[1]

M.Y. Abadi and others. 2016. TensorFlow: A System for Large-scale Machine Learning. In 12th USENIX conference on Operating Systems Design and Implementation (OSDI). 265--283.

Digital Library

[2]

Apple. 2018. Apple Watch Series official website. (2018). Retrieved July 20, 2018 from https://www.apple.com/lae/watch/

[3]

D.-A. Clevert, T. Unterthiner, and S. Hochreiter. 2015. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). ArXiv e-prints (2015).

[4]

IEEE Signal Processing Cup. 2015. Heart Rate Monitoring During Physical Exercise using Wrist-Type Photoplethysmographic (PPG) Signals. (2015). Retrieved July 20, 2018 from https://sites.google.com/site/researchbyzhang/ieeespcup2015

[5]

H. Gjoreski, J. Bizjak, M. Gjoreski, and M. Gams. 2016. Comparing Deep and Classical Machine Learning Methods for Human Activity Recognition using Wrist Accelerometer. In IJCAI-16 Workshop on Deep Learning for Artificial Intelligence (DLAI).

[6]

Y. Guan and T. Ploetz. 2017. Ensembles of Deep LSTM Learners for Activity Recognition using Wearables. In ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).

Digital Library

[7]

N. Y. Hammerla, S. Halloran, and T. Ploetz. 2016. Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables. In International Joint Conference on Artificial Intelligence (IJCAI). 1533--1540.

Digital Library

[8]

J. Hannink, T. Kautz, C. F. Pasluosta, K.-G. Gasmann, J. Klucken, and B. M. Eskofier. 2017. Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics 21, 1 (2017).

[9]

S. Ioffe and C. Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ArXiv e-prints (2015).

[10]

R. Jaafar and M. A. A. Rozali. 2017. Estimation of Breathing Rate and Heart Rate from Photoplethysmogram. In 6th International Conference on Electrical Engineering and Informatics (ICEEI).

[11]

B. S. Kim and S. K. Yoo. 2006. Motion Artifact Reduction in Photoplethysmography using Independent Component Analysis. IEEE Transactions on Biomedical Engineering 53, 3 (2006), 566--568.

[12]

D. P. Kingma and J. Ba. 2014. Adam: A Method for Stochastic Optimization. ArXiv e-prints (2014).

[13]

S. Muenzner, P. Schmidt, A. Reiss, M. Hanselmann, R. Stiefelhagen, and R. Duerichen. 2017. CNN-based Sensor Fusion Techniques for Multimodal Human Activity Recognition. In International Symposium on Wearable Computers (ISWC). 158--165.

Digital Library

[14]

T. Ploetz and Y. Guan. 2018. Deep Learning for Human Activity Recognition in Mobile Computing. Computer 51, 5 (2018).

[15]

M. R. Ram, V. M. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy. 2012. A Novel Approach for Motion Artifact Reduction in PPG Signals based on AS-LMS Adaptive Filter. IEEE Transactions on Instrumentation and Measurement 61, 5 (2012), 1445--1457.

[16]

A. Reiss and D. Stricker. 2012. Creating and benchmarking a new dataset for physical activity monitoring. In Proceedings of 5th Workshop on Affect and Behaviour Related Assistance (ABRA).

Digital Library

[17]

S. Salehizadeh, D. Dao, J. Bolkhovsky, C. Cho, Y. Mendelson, and K. Chon. 2015. A Novel Time-varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities using a Wearable Photoplethysmogram Sensor. Sensors 16, 1 (2015).

[18]

Samsung. 2018. Samsung Simband official website. (2018). Retrieved July 20, 2018 from https://www.simband.io/

[19]

T. Schaeck, M. Muma, and A. M. Zoubir. 2017. Computationally Efficient Heart Rate Estimation During Physical Exercise using Photoplethysmographic Signals. In 25th European Signal Processing Conference (EUSIPCO).

[20]

S. P. Shashikumar, A. J. Shah, Q. Li, G. D. Clifford, and S. Nemati. 2017. A Deep Learning Approach to Monitoring and Detecting Atrial Fibrillation using Wearable Technology. In IEEE EMBS International Conference on Biomedical Health Informatics (BHI). 141--144.

[21]

Z. Zhang. 2015. Photoplethysmography-based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction. IEEE Trans. Biomed. Eng. 62, 8 (2015), 1902--1910.

[22]

Z. Zhang, Z. Pi, and B. Liu. 2015. TROIKA: A General Framework for Heart Rate Monitoring using Wrist-type Photoplethysmographic Signals During Intensive Physical Exercise. IEEE Trans. Biomed. Eng. 62, 2 (2015), 522--531.

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Pedrosa-Rodriguez ACamara CPeris-Lopez P(2024)Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI TechniquesApplied Sciences10.3390/app1419894514:19(8945)Online publication date: 4-Oct-2024
https://doi.org/10.3390/app14198945
N KRavindaran RN SV S AS AS B(2024)Bimodal Framework for Cardiac Arrhythmia Analysis using Deep Learning2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)10.1109/ICECCC61767.2024.10593889(1-8)Online publication date: 2-May-2024
https://doi.org/10.1109/ICECCC61767.2024.10593889
Fuster-Barceló CGuerrero-López ACamara CPeris-Lopez P(2024)Exploring the power of photoplethysmogram matrix for atrial fibrillation detection with integrated explainabilityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108325133(108325)Online publication date: Jul-2024
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cover image ACM Conferences

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

October 2018

1881 pages

ISBN:9781450359665

DOI:10.1145/3267305

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Florida: University of Florida
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Published: 08 October 2018

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UbiComp '18

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University of Florida
SIGCHI

UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2018

Singapore, Singapore

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Pedrosa-Rodriguez ACamara CPeris-Lopez P(2024)Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI TechniquesApplied Sciences10.3390/app1419894514:19(8945)Online publication date: 4-Oct-2024
https://doi.org/10.3390/app14198945
N KRavindaran RN SV S AS AS B(2024)Bimodal Framework for Cardiac Arrhythmia Analysis using Deep Learning2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)10.1109/ICECCC61767.2024.10593889(1-8)Online publication date: 2-May-2024
https://doi.org/10.1109/ICECCC61767.2024.10593889
Fuster-Barceló CGuerrero-López ACamara CPeris-Lopez P(2024)Exploring the power of photoplethysmogram matrix for atrial fibrillation detection with integrated explainabilityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108325133(108325)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108325
Gao HZhang CPei SWu X(2024)IDTL-rPPG: Remote heart rate estimation using instance-based deep transfer learningBiomedical Signal Processing and Control10.1016/j.bspc.2024.10641695(106416)Online publication date: Sep-2024
https://doi.org/10.1016/j.bspc.2024.106416
Kim JPark SChoi S(2024)Reliable ECG analysis using recognition scores from multiple deep neural networksJournal of Mechanical Science and Technology10.1007/s12206-024-0345-038:4(2169-2178)Online publication date: 18-Apr-2024
https://doi.org/10.1007/s12206-024-0345-0
Karapinar ESevinc E(2024)A non-invasive heart rate prediction method using a convolutional approachMedical & Biological Engineering & Computing10.1007/s11517-024-03217-6Online publication date: 15-Nov-2024
https://doi.org/10.1007/s11517-024-03217-6
Staszak KTylkowski BStaszak M(2023)From Data to Diagnosis: How Machine Learning Is Changing Heart Health MonitoringInternational Journal of Environmental Research and Public Health10.3390/ijerph2005460520:5(4605)Online publication date: 5-Mar-2023
https://doi.org/10.3390/ijerph20054605
Dark ESaleem ULämsä AÁlvarez Casado CBordallo López M(2022)Heart Rate Estimation from Noisy PPGs Using 1D/2D Conversion and Transfer LearningAdjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers10.1145/3544793.3563407(163-167)Online publication date: 11-Sep-2022
https://dl.acm.org/doi/10.1145/3544793.3563407
Suleman MMotaman KHasanpoor YGhamari MAlipour KZadeh M(2022)Respiratory Events Estimation From PPG Signals Using a Simple Peak Detection Algorithm2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)10.1109/ICBME57741.2022.10052943(119-123)Online publication date: 21-Dec-2022
https://doi.org/10.1109/ICBME57741.2022.10052943
Zhang YXu JXie MWang WYe KWang JZhu D(2022)PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00294(1971-1978)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00294
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