Comparison of BCG, PCG and ECG signals in application of heart rate monitoring of the human body

Nedoma, Jan; Fajkus, Marcel; Martínek, Radek; Kepák, Stanislav; Čubík, Jakub; Žabka, Stanislav; Vašinek, Vladimír

doi:10.1109/tsp.2017.8076019

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…One of the most commonly measured vital signs is the heart rate (or pulse), i.e., the number of heartbeats per minute. Electrocardiography (ECG), photoplethysmography (PPG), ballistocardiography (BCG) and phonocardiography (PCG) are common methods of heart rate sensing [ 29 ]. On the one hand, ECG sensors record the electrical activity and rhythm of the heart, in the form of electrocardiograms, by attaching a series of electrodes to the skin.…”

Section: Sensors: Definition and Taxonomymentioning

confidence: 99%

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Batista

Moncusi

López-Aguilar³

et al. 2021

Sensors

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The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

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Section: Sensors: Definition and Taxonomymentioning

confidence: 99%

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Batista

Moncusi

López-Aguilar³

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The acquisition system of PCG, similar to ECG, consists of low noise amplifiers and filters. As this review focuses only on the ECG and related signal processing schemes, the interested reader may refer to more details regarding PCG in [56][57][58]. Ballistocardiogram measures the Ballistic forces (Mechanical Forces) generated by the heart and is a non-invasive method based on the measurement of the body motion generated by the blood's ejection at each cardiac cycle [59].…”

Section: Cardiac Health Monitoring Schemesmentioning

confidence: 99%

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Arora¹,

Mishra²

2021

Preprint

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Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG, and covers the most recent signal processing schemes with milestones over last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing Schemes and the Complex Transformations are discussed. It also provides recommendations for the inclusion of certain important points based on the review.

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“…Results have been evaluated against an electronic stethoscope which recognizes and records sound pulses generated from the cardiac activity. Nedoma et al [93] evaluated the effectiveness of the FBG sensor against fiber interferometric sensor for heart rate measurement. The former measured the heart rate through ballistocardiography, while the latter measured the heart rate through Phonocardiography.…”

Section: Fiber Optic-based Sensorsmentioning

confidence: 99%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

126

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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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Comparison of BCG, PCG and ECG signals in application of heart rate monitoring of the human body

Cited by 14 publications

References 16 publications

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Ballistocardiogram signal processing: a review

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