HRV analysis using ballistocardiogram with LabVIEW

Jose, Sajeev K.; Shambharkar, Chandan M.; Chunkath, Job

doi:10.1109/iccct2.2015.7292732

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…The ECG signal primarily consists of three main segments, first one is the P-wave, second is the QRS complex and the last or the third one is the T wave [6]. All the three components of the electrocardiogram are shown in the Figure 2 in detail, when the blood circulates towards the heart and proceeds in the atrium, known as atrial depolarization, due to which the P wave is formed [7]. After that, the blood moves for ventricular depolarization which causes the formation of QRS complex.…”

Section: Interpretation Of the Ecg Signalmentioning

confidence: 99%

Designing a simple toolbox for the early detection of arrhythmia, using advanced virtual instrumentation.

Sharma¹,

Shukla²

2019

biomedicalresearch

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An arrhythmia is a disease in which the heart may beat too fast, too early, too slow or irregular and few may give rise to death or irreparable danger. Atrial fibrillation is one of the most common types of arrhythmia; in this case the heart may race at more than 400 beats per minute. The complications due to atrial fibrillation may lead to cardiomyopathy, haemodynamic instability, strokes or even cardiac failure. The main purpose of this study is to design and develop a simple tool box for the early detection of the atrial fibrillation using the biomedical toolkit of the LabVIEW. As ECG is the conventional method of its detection, but suffers several disadvantages regarding accuracy in diagnosis, but when these vital signals are subjected to the real time analysis on a specially designed LabVIEW based toolbox, the diagnosis accuracy and the correct prediction increases several times. This study will prove to be a great panacea for the cardiac affected patients when the fatal threat to the heart, arrhythmia will be detected at a very early stages.

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Section: Interpretation Of the Ecg Signalmentioning

confidence: 99%

Designing a simple toolbox for the early detection of arrhythmia, using advanced virtual instrumentation.

Sharma¹,

Shukla²

2019

biomedicalresearch

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Section: Introductionmentioning

confidence: 99%

“…Classically, IHR is derived from electrocardiogram (ECG) signals by measuring the time interval between consecutive R peaks [4]. Alternatively, Jose et al [5] used a copper board with two piezoelectric sensors mounted inside to estimate IHR from a ballistocardiogram (BCG) and subsequently performed heart rate variability analysis. Hernandez et al [6] used the camera of a smartphone to measure contact-based photoplethysmogram (PPG) and derived IHR, achieving an absolute mean error of 0.94 ± 0.28 beats per minute (bpm).…”

Section: Introductionmentioning

confidence: 99%

Speckle Vibrometry for Instantaneous Heart Rate Monitoring

Que

Meulen

Verkruysse

et al. 2023

Sensors

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Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland–Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [−8.65, 7.79] bpm, [−9.79, 9.25] bpm, and [−10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers.

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“…Heart rate decreases with age. Cardiovascular disease not only occurs in the elderly, but also tends to be younger [2][3]. HRV refers to the variation of heartbeat cycle and is a research hotspot in ECG signal processing field in recent years.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Measurement and Mental Model Building Based on Body Area Networks

Luo

Lü

et al. 2022

Preprint

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Heart rate variability (HRV) is a specific quantitative indicator of autonomic nerve regulation of the heart. The research of HRV can quantify the changes of human mental state. In this paper, an improved differential threshold method was proposed for R wave detection and recognition of ECG signals. The recognition rate was improved by improving the starting position of R wave and the time window function of the traditional differential threshold method. The experimental platform in this paper is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Experimental results showed that the recognition rate of R wave of real-time 5min ECG data collected by this algorithm was more than 99%. Then, analytic hierarchy Process (AHP) was used to construct the mental stress assessment model, and the weight judgment matrix was constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check was carried out to obtain the weight value of the corresponding HRV analysis parameters. Finally, comparative experiment proved that the model can describe the mental stress of the body quantitatively.

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HRV analysis using ballistocardiogram with LabVIEW

Cited by 9 publications

References 3 publications

Designing a simple toolbox for the early detection of arrhythmia, using advanced virtual instrumentation.

Designing a simple toolbox for the early detection of arrhythmia, using advanced virtual instrumentation.

Speckle Vibrometry for Instantaneous Heart Rate Monitoring

Heart Rate Variability Measurement and Mental Model Building Based on Body Area Networks

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