Literature DB >> 26737646

Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?

Shadi Mahdiani, Vala Jeyhani, Mikko Peltokangas, Antti Vehkaoja.   

Abstract

With the worldwide growth of mobile wireless technologies, healthcare services can be provided at anytime and anywhere. Usage of wearable wireless physiological monitoring system has been extensively increasing during the last decade. These mobile devices can continuously measure e.g. the heart activity and wirelessly transfer the data to the mobile phone of the patient. One of the significant restrictions for these devices is usage of energy, which leads to requiring low sampling rate. This article is presented in order to investigate the lowest adequate sampling frequency of ECG signal, for achieving accurate enough time domain heart rate variability (HRV) parameters. For this purpose the ECG signals originally measured with high 5 kHz sampling rate were down-sampled to simulate the measurement with lower sampling rate. Down-sampling loses information, decreases temporal accuracy, which was then restored by interpolating the signals to their original sampling rates. The HRV parameters obtained from the ECG signals with lower sampling rates were compared. The results represent that even when the sampling rate of ECG signal is equal to 50 Hz, the HRV parameters are almost accurate with a reasonable error.

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Year:  2015        PMID: 26737646     DOI: 10.1109/EMBC.2015.7319746

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  11 in total

1.  Ultra-shortened time-domain HRV parameters at rest and following exercise in athletes: an alternative to frequency computation of sympathovagal balance.

Authors:  Michael R Esco; Henry N Williford; Andrew A Flatt; Todd J Freeborn; Fabio Y Nakamura
Journal:  Eur J Appl Physiol       Date:  2017-11-11       Impact factor: 3.078

2.  Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users.

Authors:  Abhishek Tiwari; Isabela Albuquerque; Mark Parent; Jean-François Gagnon; Daniel Lafond; Sébastien Tremblay; Tiago H Falk
Journal:  Entropy (Basel)       Date:  2019-08-10       Impact factor: 2.524

3.  Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques.

Authors:  Monika Simjanoska; Martin Gjoreski; Matjaž Gams; Ana Madevska Bogdanova
Journal:  Sensors (Basel)       Date:  2018-04-11       Impact factor: 3.576

4.  Impact of observational error on heart rate variability analysis.

Authors:  Monika Petelczyc; Jan Jakub Gierałtowski; Barbara Żogała-Siudem; Grzegorz Siudem
Journal:  Heliyon       Date:  2020-05-19

5.  Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis.

Authors:  Ohhwan Kwon; Jinwoo Jeong; Hyung Bin Kim; In Ho Kwon; Song Yi Park; Ji Eun Kim; Yuri Choi
Journal:  Healthc Inform Res       Date:  2018-07-31

6.  Non-invasive real-time autonomic function characterization during surgery via continuous Poincaré quantification of heart rate variability.

Authors:  Maddalena Ardissino; Nicoletta Nicolaou; Marcela Vizcaychipi
Journal:  J Clin Monit Comput       Date:  2018-10-03       Impact factor: 2.502

7.  Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach.

Authors:  Ivan Liu; Shiguang Ni; Kaiping Peng
Journal:  Sensors (Basel)       Date:  2020-03-30       Impact factor: 3.576

Review 8.  Wearable-Based Affect Recognition-A Review.

Authors:  Philip Schmidt; Attila Reiss; Robert Dürichen; Kristof Van Laerhoven
Journal:  Sensors (Basel)       Date:  2019-09-20       Impact factor: 3.576

9.  Continuous Vital Monitoring During Sleep and Light Activity Using Carbon-Black Elastomer Sensors.

Authors:  Titus Jayarathna; Gaetano D Gargiulo; Paul P Breen
Journal:  Sensors (Basel)       Date:  2020-03-12       Impact factor: 3.576

10.  AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.

Authors:  Bambang Tutuko; Siti Nurmaini; Alexander Edo Tondas; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ria Esafri; Firdaus Firdaus; Ade Iriani Sapitri
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-14       Impact factor: 2.796

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