Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals

Pröll, Samuel; Hofbauer, Stefan; Kolbitsch, Christian; Schubert, Rainer; Fritscher, Karl D.

doi:10.1109/embc.2019.8857731

Cited by 9 publications

(26 citation statements)

References 16 publications

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“…The location of the waves associated with systolic events can then be found using peak detection. In our previous study (Pröll et al 2019), five different approaches were evaluated on the same dataset. A BCG segmentation quality threshold is used to discard unreliable heart rate estimates.…”

Section: Related Workmentioning

confidence: 99%

“…Current research is focused on using BCG for monitoring cardiovascular activity. Several methods have been introduced to find characteristic peaks in BCG signals, which can be used to measure heart rate (Suliman et al 2019, Pröll et al 2019. Some authors attempt to use BCG in combination with PPG or ECG to estimate other cardiovascular parameters such as cardiac output or blood pressure (Kim et al 2018, Yao et al 2019.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, we use different CNN and RNN architectures for heart rate estimation from ballistocardiographic signals. The resulting estimation accuracy is compared against five traditional digital signal processing approaches investigated in a previous work (Pröll et al 2019). In addition, we try to find compact and plain architectures in order to minimize unnecessary computational load.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heart rate estimation from ballistocardiographic signals using deep learning

Pröll¹,

Tappeiner²,

Hofbauer³

et al. 2021

Physiol. Meas.

Self Cite

View full text Add to dashboard Cite

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“…Next, we extract BCG from the resulting signals of BM 0. Considering that the spectrum of a typical BCG ranges from 3 Hz to 10 Hz [31], we employ a 2nd-order Butterworth bandpass lter ranges 2.5-10 (Hz) to remove the undesired signal interference and then detect heartbeat interval by using a forward and backward approach [29].…”

Section: Preprocessing Of Vital Signsmentioning

confidence: 99%

Sleep Staging Using Noncontact-Measured Vital Signs

Wang

Zha

et al. 2022

Journal of Healthcare Engineering

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As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people’s quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient’s mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen’s Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.

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“…There are many applications of BCG/SCG in cardiac activity analysis, such as diagnosis of cardiovascular diseases [61][62][63][64], monitoring of cardiovascular status of a patient [37,[65][66][67][68][69], and assisting in evaluation of human cardiovascular health level [70][71][72]. It should be noted that ECG, as the golden standard for the means of cardiovascular monitoring, always takes the leading position [73,74], therefore, BCG/SCG research, often with reference of ECG measurement results, assists waveform reference point in signal analysis [75,76].…”

Section: Cardiac Activity Analysismentioning

confidence: 99%

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

Han

Wang

et al. 2021

Applied Sciences

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The current status of the research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the field of medical treatment, health care and nursing was analyzed systematically, and the important direction in the research was explored, to provide reference for the relevant researches. This study, based on two large databases, CNKI and PubMed, used the bibliometric analysis method to review the existing documents in the past 20 years, and made analyses on the literature of BCG and SCG for their annual changes, main countries/regions, types of research, frequently-used subject words, and important research subjects. The results show that the developed countries have taken a leading position in the researches in this field, and have made breakthroughs in some subjects, but their research results have been mainly gained in the area of research and development of the technologies, and very few have been actually industrialized into commodities. This means that in the future the researchers should focus on the transformation of BCG and SCG technologies into commercialized products, and set up quantitative health assessment models, so as to become the daily tools for people to monitor their health status and manage their own health, and as the main approaches of improving the quality of life and preventing diseases for individuals.

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Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals

Cited by 9 publications

References 16 publications

Heart rate estimation from ballistocardiographic signals using deep learning

Heart rate estimation from ballistocardiographic signals using deep learning

Sleep Staging Using Noncontact-Measured Vital Signs

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

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