A comparison of three heart rate detection algorithms over ballistocardiogram signals

Sadek, Ibrahim; Abdulrazak, Bessam

doi:10.1016/j.bspc.2021.103017

Cited by 17 publications

(21 citation statements)

References 55 publications

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“…The choice between wearable and non-wearable sensors should be based on the medical conditions of each patient group. Hence, there will always be a trade-off between data continuity and patient comfort [46]. Following our discussion, we conclude the paper in the next section.…”

Section: Discussion and Viewpointsmentioning

confidence: 89%

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Unobtrusive Monitoring of Sleep Cycles: A Technical Review

2022

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Polysomnography is the gold-standard method for measuring sleep but is inconvenient and limited to a laboratory or a hospital setting. As a result, the vast majority of patients do not receive a proper diagnosis. In an attempt to solve this issue, sleep experts are continually looking for unobtrusive and affordable alternatives that can provide longitudinal sleep tracking. Collecting longitudinal data on sleep can accelerate epidemiological studies exploring the effect of sleep on health and disease. These alternatives can be in the form of wearables (e.g., actigraphs) or nonwearable (e.g., under-mattress sleep trackers). To this end, this paper aims to review the several attempts made by researchers toward unobtrusive sleep monitoring, specifically sleep cycle. We have performed a literature search between 2016 and 2021 and the following databases were used for retrieving related articles to unobtrusive sleep cycle monitoring: IEEE, Google Scholar, Journal of Clinical Sleep Medicine (JCSM), and PubMed Central (PMC). Following our survey, although existing devices showed promising results, most of the studies are restricted to a small sample of healthy individuals. Therefore, a broader scope of participants should be taken into consideration during future proposals and assessments of sleep cycle tracking systems. This is because factors such as gender, age, profession, and social class can largely affect sleep quality. Furthermore, a combination of sensors, e.g., smartwatches and under-mattress sleep trackers, are necessary to achieve reliable results. That is, wearables and nonwearable devices are complementary to each other, and so both are needed to boost the field of at-home sleep monitoring.

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Section: Discussion and Viewpointsmentioning

confidence: 89%

“…In addition, if the sensor has a short battery life, it needs to be removed frequently for charging. These situations will undoubtedly distract patients and similarly disrupt the data collection [46,47]. The choice between wearable and non-wearable sensors should be based on the medical conditions of each patient group.…”

Section: Discussion and Viewpointsmentioning

confidence: 99%

Unobtrusive Monitoring of Sleep Cycles: A Technical Review

2022

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“…Optical sensors based on the principle of microbend loss, in which light intensity loss occurs when an optical fiber is bent, are simple in structure and can be used to measure changes in force and displacement. [156] In the field of wearable biosensors, such sensors can be used for pulse and blood pressure monitoring. As displayed in Figure 16f, Zhu et al [156b] obtained stretchable and ultrathin optical sensors by embedding wavy microfibers into ultrathin PDMS films.…”

Section: Optical Sensorsmentioning

confidence: 99%

Biocompatible Material‐Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems

Liu

Batool

et al. 2023

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Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material‐to‐device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in‐sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.

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“…Different methods of heart rate detection from BCG signals have been proposed using time domain or timefrequency domain approaches such as template matching [5], continuous wavelet transform (CWT) based peak detection [5], as well as machine learning based approaches [6]. Some of the drawbacks of these methods such as requiring annotated data to train a model [6] can be challenging when considering long-term measurements, where it may be impractical to collect enough time synced ECG data which would allow for correct beat labeling.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Bed Based Ballistocardiography Heart Rate Estimation using Continuous Wavelet Transforms and Autocorrelation

Elnaggar¹,

Hurnanen²,

Sandelin³

et al. 2022

Computing in Cardiology Conference (CinC)

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Bed based ballistocardiography (BCG) is a prime candidate for at home and nighttime monitoring especially in the growing elderly population because co-operation from the user is not required to be able to record signals.One issue with BCG is that the signal quality has intraand inter-person variability based on factors such as age, gender, body position, and motion artifacts, making it challenging to accurately measure heart rate.A rule-based algorithm which considers all eight available BCG channels simultaneously from a given time epoch was developed using continuous wavelet transform (CWT) to extract the localized time-frequency representation of each epoch and then an averaging method was applied across the different scales of the CWT to produce a 1-dimensional array. Autocorrelation was then applied to this array to produce a heart rate estimate based on the lag between the autocorrelation maximum and the first side peak. This method does not require identification of individual heart beats to estimate heart rate and does not require annotated training data.This model produces an average mean absolute error (MAE) of 1.09 bpm across 40 subjects when compared to heart rate derived from ECG. This method produces competitive results without the need for annotated training data, which can be challenging to collect.

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A comparison of three heart rate detection algorithms over ballistocardiogram signals

Cited by 17 publications

References 55 publications

Unobtrusive Monitoring of Sleep Cycles: A Technical Review

Unobtrusive Monitoring of Sleep Cycles: A Technical Review

Biocompatible Material‐Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems

Multichannel Bed Based Ballistocardiography Heart Rate Estimation using Continuous Wavelet Transforms and Autocorrelation

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