Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion

Zhu, Yongtian; Fook, Victor Foo Siang; Jianzhong, Emily Hao; Jayachandran, M.; Guan, Cuntai; Zhang, Haihong; Jiliang, Eugene Phua; Biswas, Jit

doi:10.1109/embc.2014.6944838

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…Brüser et al (2013) use modified versions of interval estimators originally used for pitch tracking. Other works compute the cepstrum of small signal windows to obtain the heart rate (Kortelainen et al 2012, Zhu et al 2014. In addition, various methods have been introduced for locating individual heartbeats in the BCG, which also can be used to calculate the heart rate from BCG signals.…”

Section: Related Workmentioning

confidence: 99%

Heart rate estimation from ballistocardiographic signals using deep learning

Pröll¹,

Tappeiner²,

Hofbauer³

et al. 2021

Physiol. Meas.

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Objective. Ballistocardiography (BCG) is an unobtrusive approach for cost-effective and patient-friendly health monitoring. In this work, deep learning methods are used for heart rate estimation from BCG signals and are compared against five digital signal processing methods found in literature. Approach. The models are evaluated on a dataset featuring BCG recordings from 42 patients, acquired with a pneumatic system. Several different deep learning architectures, including convolutional, recurrent and a combination of both are investigated. Besides model performance, we are also concerned about model size and specifically investigate less complex and smaller networks. Main results. Deep learning models outperform traditional methods by a large margin. Across 14 patients in a held-out testing set, an architecture with stacked convolutional and recurrent layers achieves an average mean absolute error (MAE) of 2.07 beat min−1, whereas the best-performing traditional method reaches 4.24 beat min−1. Besides smaller errors, deep learning models show more consistent performance across different patients, indicating the ability to better deal with inter-patient variability, a prevalent issue in BCG analysis. In addition, we develop a smaller version of the best-performing architecture, that only features 8283 parameters, yet still achieves an average MAE of 2.32 beat min−1 on the testing set. Significance. This is the first study that applies and compares different deep learning architectures to heart rate estimation from bed-based BCG signals. Compared to signal processing algorithms, deep learning models show dramatically smaller errors and more consistent results across different individuals. The results show that using smaller models instead of excessively large ones can lead to sufficient performance for specific biosignal processing applications. Additionally, we investigate the use of fully convolutional networks for 1D signal processing, which is rarely applied in literature.

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Section: Related Workmentioning

confidence: 99%

Heart rate estimation from ballistocardiographic signals using deep learning

Pröll¹,

Tappeiner²,

Hofbauer³

et al. 2021

Physiol. Meas.

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“…Ballistocardiography, as the measurement of the body's micromovements due to blood ejected from the heart and moved in the large vessels, is a promising alternative for pulse rate and heart rate variability (HRV) assessment (12)(13)(14)(15)(16)(17)(18)(19)(20). Current technologies such as electrocardiography (ECG) or echocardiography proved in the past to be more reliable in clinical practice because of better reproducibility and fewer interindividual differences.…”

Section: Heart Rate Estimation From Bcgmentioning

confidence: 99%

“…On the other hand, they also turned out to be uncomfortable for the patients, as either skin-electrode interfaces or flexible bandages could cause skin irritations after long use (21). BCG is instead causing increasing research interest mostly due to the advancement of vibration sensing technology, improved unobtrusiveness of the sensing devices, and increased computational power, which provides clear advantages in terms of ease of use, comfort level, and cost, thus allowing for wide applications at homes or hospitals (18)(19)(20)(21)(22). The presence of strong variation for what concerns the morphology of individual heartbeats still turns out to be a key challenge for a correct analysis of BCG.…”

Section: Heart Rate Estimation From Bcgmentioning

confidence: 99%

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Tramontano,

Tamburis,

Cioce

et al. 2023

Front. Digit. Health

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Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

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“…Otis et al used a new generation of mattress-based fiber-optic sensor for heart rate monitoring [17]. In [17], the peak searching algorithm, EMD, clustering and cepstrum methods [25] were compared for heart-rate detection. Aitkulov et al [18] proposed a sensor based on the integration of a smartphone with a plastic optical fiber by using the flashlight as the source and the camera as a photodetector, and they applied the Fast Fourier Transformation (FFT) to detect heart rate.…”

Section: Related Workmentioning

confidence: 99%

Detection of Sleep Biosignals Using an Intelligent Mattress Based on Piezoelectric Ceramic Sensors

Peng

Ding

Wang

et al. 2019

Sensors

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Physiological information such as respiratory rate and heart rate in the sleep state can be used to evaluate the health condition of the sleeper. Traditional sleep monitoring systems need body contact and are intrusive, which limits their applicability. Thus, a comfortable sleep biosignals detection system with both high accuracy and low cost is important for health care. In this paper, we design a sleep biosignals detection system based on low-cost piezoelectric ceramic sensors. 18 piezoelectric ceramic sensors are deployed under the mattress to capture the pressure data. The appropriate sensor that captures respiration and heartbeat sensitively is selected by the proposed channel-selection algorithm. Then, we propose a dynamic smoothing algorithm to extract respiratory rate and heart rate using the selected data. The dynamic smoothing can separate heartbeat signals from respiratory signals with low complexity by dynamically choosing the smooth window, and it is suitable for real-time implementation in low-cost embedded systems. For comparison, wavelet analysis and ensemble empirical mode decomposition (EEMD) are performed in a personal computer (PC). Experimental results show that data collected by piezoelectric ceramic sensors can be used for respiratory-rate and heart-rate detection with high accuracy. In addition, the dynamic smoothing can achieve high accuracy close to wavelet analysis and EEMD, while it has much lower complexity.

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Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion

Cited by 12 publications

References 5 publications

Heart rate estimation from ballistocardiographic signals using deep learning

Heart rate estimation from ballistocardiographic signals using deep learning

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Detection of Sleep Biosignals Using an Intelligent Mattress Based on Piezoelectric Ceramic Sensors

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