Demo: BCG Measurement by differential Sensing in Real-Time

Kulau, Ulf; Rust, Jochen; Albrecht, Urs‐Vito

doi:10.1109/dcoss54816.2022.00025

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Custom implementation was necessary to support differential Seismocardiogram (SCG) sensing for each patch and ensure time-synchronous acquisition of ECG data, allowing for clock-synchronous interfaces (see Figure 2 ). The system is described in detail in [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study

Pustozerov,

Kulau,

Albrecht

2024

Bioengineering

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In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity—reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.

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Section: Methodsmentioning

confidence: 99%

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study

Pustozerov,

Kulau,

Albrecht

2024

Bioengineering

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“…The hardware used for the BCG measurements has been described in [2,3]. The software is designed to perform BCG measurements quickly and with high data quality.…”

Section: Methodsmentioning

confidence: 99%

On-Site Visualization of Ballistocardiography Data

Albrecht

Kulau

2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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“…Several different types of sensors can be used for BCG measurements during sleep, and the number and types of sensors continue to evolve as new technology emerges. Pressure/force-based sensors such as force-sensitive resistors (FSR) [34,35], load cells [36], inertial sensors such as accelerometers, gyroscopes, and magnetometers [37,38], capacitive micromachined ultrasound transducers (CMUTs) [39], piezoelectric sensors [40], and strain gauges [41] have been tested and used in cardiorespiratory measurement during sleep.…”

Section: Introductionmentioning

confidence: 99%

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study

Haghi

Asadov

Boiko

et al. 2023

Sensors

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Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

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Demo: BCG Measurement by differential Sensing in Real-Time

Cited by 5 publications

References 6 publications

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study

On-Site Visualization of Ballistocardiography Data

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study

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