Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases

Sieciński, Szymon; Tkacz, Ewaryst; Kostka, P.

doi:10.3390/s23042152

Cited by 8 publications

(8 citation statements)

References 77 publications

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“…This study demonstrated that acceptable HRV indices can be computed from NCC local maxima provided by the ECG-free heartbeat detection method based on template matching, which overcomes potential issues in localizing SCG/GCG fiducial points. It is also worth underlining that only [88][89][90] analyzed signals from pathological subjects, while all other studies focused on small cohorts of healthy subjects. In addition, many of these studies do not report the number of inter-beat intervals considered for HRV analysis, which impacts the reliability of the statistical analyses.…”

Section: Discussionmentioning

confidence: 99%

“…This aspect makes heartbeat detection a much more complex task. Few studies by Sieci ński et al investigated the feasibility of HRV analysis on cardio-mechanical signals of pathological subjects [88][89][90]. Specifically, HRV indices obtained from the SCG and/or GCG signals of 30 healthy volunteers and 30 patients with valvular heart diseases (VHDs) were compared with those computed from reference ECG signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Discussionmentioning

confidence: 99%

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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“…Each of these waves carries information about heart wellness [3,4]. For instance, although it is not possible to fully analyze heart wellness by analyzing only the R waves, the R-peaks and, more precisely, the intervals between R-peaks and their variability, called the Heart Rate Variability (HRV), carry enough information to diagnose several heart diseases, including arrhythmia [5] and valvular heart diseases [6].…”

Section: A Ecg Signal Backgroundmentioning

confidence: 99%

“…6 presents the implementation of the R-peak detector. This is the main component of the system, as R-peaks carry enough information to diagnose several heart diseases, such as arrhythmia [5] and valvular heart diseases [6].…”

Section: R-peak Detector Circuitmentioning

confidence: 99%

A Low Power R-peak Detector Clocked at Signal Sampling Rate

Dutra,

H. C. Ferreira,

D. Colletta

et al. 2024

JICS

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This paper presents a real-time, low-power R-peak detector implemented in an FPGA. It is different from other implementations as it runs at the same signal sampling rate, rather than utilizing a high clock frequency as utilized in batch processing with high throughput systems. Such implementation relies on a Savitzky-Golay filter for power line noise filtering, and on an adapted version of the Difference Operation Method (DOM) algorithm. The modification in DOM is needed in order to be able to process the data either without increasing the clock to process data in a batch fashion or unsustainably increasing latency. It uses the Savitzky-Golay Digital Differentiator, eliminating further filtering stages. The prototype was characterized using both the MIT-BIH database and Fluke Prosim 8 Vital Signal Simulator. The proposed system features a high degree of matched R-peaks even being extremely efficient regarding power dissipation. Moreover, it shows similar performance when compared to the original Difference Operation Method implementation.The whole system consumes 260- uW operating at 192-Hz in an FPGA model 10M50DAF484C7G, which belongs to the MAX10 family of Altera devices.

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“…The existing body of literature has exploited HR and HRV in the studies of cardiovascular health and disease (Stein et al 2007, Haensel et al 2008, Thayer et al 2010, Hillebrand et al 2013, Soares-Miranda et al 2014, physical fitness (Plews et al 2013, Mongin et al 2022, mental health (Kemp and Quintana 2013, Quintana and Heathers 2014, Beauchaine and Thayer 2015, Pham et al 2021, and cognitive impairments (Luft et al 2009, Quintana et al 2012, Forte et al 2019. The use of HR and HRV in research and practice is expected to rapidly expand given the remarkable advances in wearable sensing technology to conveniently measure the physiological signals bearing HR and HRV, including electrocardiogram (ECG) (Sieciński et al 2020, Pham et al 2021, Parreira et al 2023, photoplethysmogram (PPG) (Lu et al 2008, Uçar et al 2018, and seismocardiogram (SCG) (Hurnanen et al 2017, Sieciński et al 2020 and ballistocardiogram (BCG) (Shin et al 2011, Brüser et al 2013 to list a few.…”

Section: Introductionmentioning

confidence: 99%

Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram

Zhou,

Lindsey,

Snyder

et al. 2024

Physiol. Meas.

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Objective: To develop analytical formulas which can serve as quantitative guidelines for the selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart rate (HR) and heart rate variability (HRV) with a desired level of accuracy. Approach: We developed analytical formulas which relate the ECG sampling rate to conservative bounds on HR and HRV errors: (i) one relating HR and sampling rate to a HR error bound and (ii) the others relating sampling rate to HRV error bounds (in terms of root-mean-square of successive differences (RMSSD) and standard deviation of normal sinus beats (SDNN)). We validated the formulas using experimental data collected from 58 young healthy volunteers which encompass a wide HR and HRV ranges through strenuous exercise. Main results: The results strongly supported the validity of the analytical formulas as well as their tightness. The formulas can be used to (i) predict an upper bound of inaccuracy in HR and HRV for a given sampling rate in conjunction with HR and HRV as well as to (ii) determine a sampling rate to achieve a desired accuracy requirement at a given HR or HRV (or its range). Significance: HR and its variability (HRV) derived from the ECG have been widely utilized in a wide range of research in physiology and psychophysiology. However, there is no established guideline for the selection of the sampling rate for the ECG required to calculate HR and HRV with a desired level of accuracy. Hence, the analytical formulas may guide in selecting sampling rates for the ECG tailored to various applications of HR and HRV.

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Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases

Cited by 8 publications

References 77 publications

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

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

A Low Power R-peak Detector Clocked at Signal Sampling Rate

Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram

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