Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Cui, Huiying; Wang, Zhongyi; Yu, Bin; Jiang, Fangfang; Geng, Ning; Li, Yongchun; Xu, Lisheng; Zheng, Dingchang; Zhang, Biyong; Lu, Peilin; Greenwald, Stephen E.

doi:10.3390/s22062423

Cited by 6 publications

(2 citation statements)

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“…While medical conditions [2,7] and information about atypical signals [2] might be included as annotations on the signal as a whole, labels for specific anomalies are often omitted. While Heart rate and heart rate variability can be reliably extracted using signal processing methods [8,9], more complex features and anomalies are more challenging to extract [10]. Furthermore, there is considerable uncertainty regarding the interpretation of BCG waveform, leading to various different interpretations over the years [10].…”

Section: Introductionmentioning

confidence: 99%

Conception of an AI-based recommender system for labeling ballistocardiographic data

Lucas,

Andreas,

Edwin

2024

Preprint

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This paper proposes a tool for assisting in the labeling ballistocardiographic data, as such data is rare. There are not many databases and those that we found are barely labeled or not at all. As such, we are developing an ai-based system that can reliably recognize anomalous areas and display them for evaluation. The system is supposed to detect multiple types of anomalies (point, collective and contextual), which can then be rejected, modified and labeled by medical professionals or researchers. For initial testing, the AER-network [1] was used on the database recorded by Carlson et al. [2] and preliminary results show that it is able to detect anomalous areas. While our system is still in its initial stage, further investigations lead us to conclude that such a system would be of great value to the medical community both in practice and in research.

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Section: Introductionmentioning

confidence: 99%

Conception of an AI-based recommender system for labeling ballistocardiographic data

Lucas,

Andreas,

Edwin

2024

Preprint

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“…In this regard, continuous, long-term monitoring would benefit from wearable devices, especially in daily life environments, all the more so if one considers that accelerometers and gyroscopes are largely integrated into smartphones nowadays. For these reasons, many studies investigated the suitability of cardio-mechanical signals for HRV analysis, particularly BCG [73][74][75], SCG [36,57,[76][77][78], and GCG [76,79]. Specific peaks or valleys of these signals, which correlate with important cardiac cycle events, were considered as heartbeat fiducial points, e.g., the aortic valve opening (AO) on SCG/GCG signals and the peak of the J wave on BCG signals.…”

Section: Introductionmentioning

confidence: 99%

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Parlato,

Centracchio,

Esposito

et al. 2023

Sensors

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Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

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Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening

Geng

et al. 2023

Computers in Biology and Medicine

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Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Cited by 6 publications

References 50 publications

Conception of an AI-based recommender system for labeling ballistocardiographic data

Conception of an AI-based recommender system for labeling ballistocardiographic data

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening

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