BCG Signal Research Articles

1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals

1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals

BCG Signal Quality Assessment Based on Time-Series Imaging Methods

BCG Signal Quality Assessment Based on Time-Series Imaging Methods

Heart rate detection of ballistocardiogram based on improved DAE and template matching method

Ballistocardiogram (BCG) is a non-invasive physiological signal detection method that can be used for non-contact detection of resting heart rate (RHR) and has been widely used in human health monitoring. However, the BCG signal is vulnerable to noise, making it challenging to accurately measure heart rate. In this paper, we propose a noise reduction model for the BCG signal based on Gramian Angular Field (GAF) and an improved Denoising Autoencoder (DAE), referred to as the GDAE model, to accurately detect heart rate in noise-contaminated signals. First, the Gramian Angular Field transform is used to convert the one-dimensional BCG signal into two-dimensional image information, highlighting the difference between the signal centroid information and noise information; after that, the transformed image is denoised by the Denoising Autoencoder to obtain a denoised BCG signal. After noise reduction, the heart rate of the subject is calculated using the adaptive template matching method. The test proves that under the strong noise interference, the proposed method improves the recall by 6.87% and the accuracy by 6.02% compared with the traditional method, indicating a better detection effect. Furthermore, the comparison test shows that the GDAE model has a significant noise reduction effect on the BCG signal, which improves the practicality of the BCG method for heart rate detection.

Blood pressure monitoring with piezoelectric bed sensor systems

ObjectiveBallistocardiography (BCG) measures vital signals without direct contact, which shows great potential for continuous sleep monitoring. This study proposed a BCG-based blood pressure (BP) estimation algorithm framework using piezoelectric bed sensor systems. Methods: To derive BP, a combination of morphological features, spectral features, and fractal dimensions of the BCG signal were utilized. Bayesian neural networks were employed to weigh the contribution of each feature and generate input-dependent coefficients. Two data balancing procedures were tested, and the proposed system's effectiveness was evaluated, including in-hospital patients and healthy subjects. Transfer learning techniques were employed to further improve the system's performance and showcase the similarity between bed systems with different piezoelectric sensors. Results: The combination of morphological and spectral features significantly improves BP estimation accuracy. The fractal dimension captures short-term BP fluctuations, improving intra-subject BP trend estimation. For young and healthy subjects, calibration-free mean absolute error (MAE) for systolic BP (SBP) and diastolic BP (DBP) is 4.20/4.25 mmHg at a 5-second time resolution. In the case of in-hospital patients, the best MAE for overnight SBP/DBP is 9.96/7.59 mmHg. Transfer learning, combined with data balancing techniques, substantially enhances BP estimation accuracy for in-hospital patients, providing insights for future algorithm designs. Conclusion: The study concludes that bed-sensor systems can track BP changes in relatively healthy subjects. However, BCG alone may not be sufficient for subjects with severe cardiovascular dysfunctions to obtain reliable BP readings. Transfer learning and proper data balancing may facilitate the fast development of new bed sensor systems with similar technologies.

Virtual sports rehabilitation and monitoring system for the elderly based on intelligent interaction and embedded system

This article uses medical electrode pads as the ECG sensor, introduces the signal into the subsequent conditioning circuit, sticks to the electrode pads on both sides of the human body's chest and abdomen, and ensures a firm fit. After each signal acquisition module works normally, connect each module to the wireless body area network terminal node for system networking test. System testing includes not only signal acquisition testing and network communication testing, but also host computer software testing. The BCG signal is collected by a piezoelectric film sensor, which converts the mechanical vibration in the human body into a variable voltage output. The results show that the system can accurately collect and process data, maintain good communication with the software platform, and has high reliability.

Real-World Consumer-Grade Sensor Signal Alignment Procedure Applied to High-Noise ECG to BCG Signal Synchronization.

In recent years, consumer-grade sensors that measure health relevant physiological signals have become widely available and are increasingly used by consumers and researchers alike. While this allows for multiple novel, potentially highly beneficial, large-scale health monitoring applications, quality of these data streams is oftentimes suboptimal. This makes alignment of different high-frequency data streams from multiple, non-connected sensors, a difficult task. In this work we describe a noise-robust framework to align high-frequency signals from different sensors, that share some underlying characteristic, obtained in a free-living, non-clinical, home environment. We demonstrate the approach on the basis of a single-lead, medical-grade, mobile electrocardiography device and a consumer-grade sleep sensor that allows for ballistocardiography. Both commercially available sensors measure the physiological process of a heartbeat. We show, on the basis of real-world data with multiple people and sensors, that the two highly noisy and sometimes dissimilar signals could in most cases be aligned with considerable precision. As a result, we could reduce mean heartbeat peak-to-peak difference by 58.1% on average and increase signal correlation by 0.40 on average.

A Mattress System of Recognizing Sleep Postures Based on BCG Signal

Sleep posture recognition is the core index of diagnosis and treatment of positional sleep apnea syndrome. In order to detect body postures noninvasively, we developed a portable approach for sleep posture recognition using BCG signals with their morphological difference. A type of piezo-electric polymer film sensor was applied to the mattress to acquire BCG, the discrete wavelet transform with cubic B-spline was used to extract characteristic parameters and a naive Bayes learning phase was adapted to predict body postures. Eleven healthy subjects participated in the sleep simulation experiments. The results indicate that the mean error obtained from heart rates was 0.04±1.3 beats/min (±1.96 SD). The final recognition accuracy of four basic sleep postures exceeded 97%, and the average value was 97.9%. This measuring system is comfortable and accurate, which can be streamlined for daily sleep monitoring application.

Research on Distributed Intelligent Mattress on the Internet of Things

Intelligent mattress is a hot research topic in the smart health care field based on Internet of things technology. At first, Non-contact sensor, BCG signal extraction, human life signal analysis, and large data analysis technologies are introduced, and then cloud platform storage and extraction technologies are also integrated into the system. Secondly, the function analysis, application principle, application network, interface design, and application cases of intelligent mattress are proposed in detail. This research is of great significance to intelligent health care and intelligent medical treatment.

BCG Artifact Removal Using Improved Independent Component Analysis Approach

<p>Recent advancement in bio-medical field has attracted researchers toward BCG signal processing for monitoring the health activities. There have been various techniques for monitoring physical activities such as (SCG) Seismocardiography, Electrocardiography (ECG) etc. BCG signal is a measurement of reaction force applied for cardiac ejection of blood. Various measurement schemes and systems have been developed for BCG detection and measurement such as tables, beds, weighing scale and chairs. Weighing scales have been promising method for measurement of BCG signal because of less cost of implementation, smaller size etc. but these devices still suffer from the artifact which are induced due to subject movement or motion during signal acquisition or it can be caused due to floor vibrations. Artifact removal is necessary for efficient analysis and health monitoring. In this work we address the issue of artifact removal in BCG signal by proposing a novel method of signal processing. According to proposed approach raw signal is pre-processed and parsed to independent component analysis which provides the decomposed components and later k-means is applied to detect the components which are responsible for artifact and removed. Proposed approach is compared with existing method and shows better performance in terms of artifact removal.</p>

FPGA Implementation of BCG Signal Filtering Scheme by Using Weight Update Process

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BCG Signal Intelligent Diagnosis Method Based on Cloud Model

BCG Signal Intelligent Diagnosis Method Based on Cloud Model

무구속 심탄도 모니터링 시스템을 이용한 스트레스 분석 기초연구

Heart related diseases mainly caused by heavy work load and increasing stress in human daily life. Therefore, researches on mobile healthcare monitoring for daily life has been carried out. Notably, wearable healthcare monitoring system which has least restriction has been tried to provide an emergency alert of abnormal heart rate. In this study, we developed chair type unconstrained BCG measurement system which able to perform continuous heart status monitoring at the office and daily life in the unconstrained way. Furthermore, adaptive threshold is used to detect the heart rate from BCG signals. The HRV(heart rate variability) is calculated from heart rate interval. ECG signal measured using conventional method and BCG signal measured using unconstraint system are carried out simultaneously for the purpose of performance evaluation. From the comparison result, BCG signal shows a similar heart beat characteristic as ECG signal. This proves the possibility of practical implementation of unconstraint healthcare monitoring system. In addition, medical examination like valsalva maneuver is performed to observe the changes in HRV due to stress. By performing valsalva maneuver, heart is said to be placed under an artificial physical stress condition. Under this artificial physical stress condition, the time and frequency domain of HRV parameters are evaluated.