Non-invasive BCG monitoring for non-traditional settings

Pino, Esteban J.; Larsen, C.W.; Chávez, Javier A. P.; Aqueveque, Pablo

doi:10.1109/embc.2016.7591795

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Wavelet transform can deal with nonlinear and non-stationary signals well, and it is an ideal method to analyze BCG signals. Currently, there have been many studies on the application of wavelet transform to biological signal preprocessing, including BCG-related studies [ 39 , 42 ]. The mother wavelet Daubechie 6 was used in our work to decompose the original BCG signal and obtain seven detail components (D1 to D7) according to the properties of each wavelet basis and its similarity with the BCG signal.…”

Section: Methodsmentioning

confidence: 99%

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Wan

Huang

Wang

2021

Sensors

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Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

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Section: Methodsmentioning

confidence: 99%

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Wan

Huang

Wang

2021

Sensors

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“…The wavelet analysis was preferred to reconstruct the signal as it produced a higher effective measurement time. A similar approach was also proposed by Pino et al [42]. However, they increased the size of the dataset to 114 people.…”

Section: Electromechanical Film-based Sensorsmentioning

confidence: 98%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

127

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There are several algorithms for analyzing and interpreting cardiorespiratory signals obtained from in-bed based sensors. In sum, these algorithms can be broadly grouped into three categories: time-domain algorithms, frequency-domain algorithms, and wavelet-domain algorithms. A summary of these algorithms is given below to highlight which category of algorithms will be used in our analysis. First, time-domain algorithms are mainly focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram signal. However, this approach has many limitations because of the nonlinear and nonstationary behavior of the ballistocardiogram signal. The implication is that the ballistocardiogram signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected. In other words, the selected component contains only information about the heart cycles or respiratory cycles, respectively. Interbeat intervals can be found easily by applying a simple peak detector. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property. Furthermore, the training step should be repeated whenever the data collection protocol has been changed.

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“…A comparativelysystematic solution to lower these prices is to turn the prominence from authorized care in healthcare systems and care houses to unauthorized care in privatized homes. Investigations [68] disclosed that manypeople requiring care prefer less interruptive unauthorized care. The normal discernment is that unauthorized care ofthe house is inexpensive.…”

Section: Analysis Of Health Smart Homesmentioning

confidence: 99%

“…SH (Smart Home) technique focuses to help people geta good quality life and to confirm elderly to survive independently and comfortably [68]. The technology of SH is recognized as a solution to lower the cost of care and living and to enhance the life's quality for people needing care.…”

Section: Analysis Of Health Smart Homesmentioning

confidence: 99%

An analytical review for cloud computing in healthcare system based biosensors

Awsaj¹,

Mashhadany

Fourati³

2022

ijhs

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For patients with geriatric medical specialty disorders equivalent to dementia, patients with ingrained devices such as pacemakers. The quantity of exercise and therefore the amount of daylight are important guides for dosing and treatment, thus observation daily health info is important to patient safety and health. A portable, wearable device and server configuration to watch information are required to supply these services to patients. The true period of time condition monitoring systems should embody GPS, accelerometer, and lightweight sensor, and might acquire health information in real-time by measure position for patients, the amount of exercise, the amount of sunlight, heart rate, blood pressure, the amount of atomic number within the blood, and different devices are often supplemental in step with the wants of the patient's condition. The server system includes sensor information, analysis rule and an internet server that's employed by the practitioner and the guardian to watch the sensor data noninheritable from the good sensors.

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Non-invasive BCG monitoring for non-traditional settings

Cited by 8 publications

References 6 publications

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Ballistocardiogram signal processing: a review

An analytical review for cloud computing in healthcare system based biosensors

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