Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Landreani, Federica; Faini, Andrea; Martín-Yebra, Alba; Morri, Mattia; Parati, Gianfranco; Caiani, Enrico Gianluca

doi:10.3390/s19173729

Cited by 23 publications

(15 citation statements)

References 42 publications

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“…It is therefore suitable for outpatient monitoring and for situations wherein results are urgently needed (Baek et al, 2015). Recently, the use of ultra-short HRV analysis based on recordings shorter than 5 min has also been studied to increase the applicability of HRV analysis according to the development of wearable devices for monitoring public health (Baek et al, 2015;Pecchia et al, 2018;Landreani et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Declining Trends of Heart Rate Variability According to Aging in Healthy Asian Adults

Choi

Cha

Park

2020

Front. Aging Neurosci.

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Heart rate variability (HRV) indices correlate with aging and are related to the autonomic nervous system. However, the trend of HRV with age has not been explored for the Asian population. Therefore, we proposed a linear regression model of HRV indices that decreased with aging in healthy Asian adults. HRV parameters [High frequency (HF), Low frequency (LF), Very low frequency (VLF), Total power (TP), HRV triangular index (HRV-index), Standard deviation of the normal-to-normal interval (SDNN), and Proportion of normal-to-normal intervals greater than 50 ms (pNN50)] were measured in a total of 300 healthy participants (150 men and 150 women) aged 19–69 years stratified into five age groups: 19–29, 30–39, 40–49, 50–59, and 60–69 years comprising 60 people each in Seoul, South Korea. A simple regression analysis was performed to reveal the linear declining trend of HRV indices with age. Independent t-tests were conducted to investigate the gender differences in HRV values depending on each age group. The values of all HRV indices showed a decreasing trend with age in healthy Korean adults, as observed in the Western population (P < 0.001 for all indices); HF (Y = −0.039x + 6.833, R2 = 0.287), LF (Y = −0.047x + 7.197, R2 = 0.414), VLF (Y = −0.025x + 6.861, R2 = 0.177), TP (Y = −0.034x + 8.082, R2 = 0.352), HRV-index (Y = −0.125x + 15.628, R2 = 0.298), SDNN (Y = −0.502x + 53.907, R2 = 0.343), and pNN50 (Y = −0.650x + 53.852, R2 = 0.345) all decreased with age. There was no significant gender difference in any HRV parameter. A linear regression model of various HRV indices has been presented considering the age of healthy Asians, which may be useful to prevent diseases related to the autonomic nervous system by estimating or tracking autonomic functional degeneration in the Asian population.

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Section: Introductionmentioning

confidence: 99%

Declining Trends of Heart Rate Variability According to Aging in Healthy Asian Adults

Choi

Cha

Park

2020

Front. Aging Neurosci.

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“…For the extraction of mean HR and RESP, three frequency domain approaches (FFT, STFT and Adjusted-FFT) were applied, to minimize the problem of nonlinear and nonstationary behavior in the signals, as well as the problem of missing peaks, especially for cardiac activity, due to non-constant sampling rate of the inertial measurements units. In fact, alternative approaches based on time domain analysis [ 26 , 27 ], mainly focused on detecting local maxima or local minima using a moving window on the acquired signals, could be more easily affected by superimposed noise, thus resulting in missing or wrong detections. The FFT method provides the power frequency information over the entire signal acquisition duration; it is less robust in presence of not perfect stationary conditions, as the normal heart rate variability.…”

Section: Discussionmentioning

confidence: 99%

“…These subtle motions associated with cardiac activity are imperceptible to the human eye but not to the sensor resolution of current micro-electromechanical systems technology embedded in wearable devices. When positioned at specific body locations, ACC and GYR sensors have shown their potential to capture peripheral motion associated with cardiac activity [ 24 ], also when embedded in common smartphone devices [ 25 , 26 ], and even being able to capture changes in sympathovagal balance induced by an external stressor [ 27 ], thus representing a complementary solution to assess the mechanical cardiac function and respiratory activity in a non-intrusive and electrodes-free way [ 28 ], while the subject is remaining relatively “still” [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Heart Rate and Respiratory Rate Estimation by Inertial Sensors Embedded in a Virtual Reality Headset

Floris

Solbiati

Landreani

et al. 2020

Sensors

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Virtual reality (VR) headsets, with embedded micro-electromechanical systems, have the potential to assess the mechanical heart’s functionality and respiratory activity in a non-intrusive way and without additional sensors by utilizing the ballistocardiographic principle. To test the feasibility of this approach for opportunistic physiological monitoring, thirty healthy volunteers were studied at rest in different body postures (sitting (SIT), standing (STAND) and supine (SUP)) while accelerometric and gyroscope data were recorded for 30 s using a VR headset (Oculus Go, Oculus, Microsoft, USA) simultaneously with a 1-lead electrocardiogram (ECG) signal for mean heart rate (HR) estimation. In addition, longer VR acquisitions (50 s) were performed under controlled breathing in the same three postures to estimate the respiratory rate (RESP). Three frequency-based methods were evaluated to extract from the power spectral density the corresponding frequency. By the obtained results, the gyroscope outperformed the accelerometer in terms of accuracy with the gold standard. As regards HR estimation, the best results were obtained in SIT, with Rs2 (95% confidence interval) = 0.91 (0.81−0.96) and bias (95% Limits of Agreement) −1.6 (5.4) bpm, followed by STAND, with Rs2 = 0.81 (0.64−0.91) and −1.7 (11.6) bpm, and SUP, with Rs2 = 0.44 (0.15−0.68) and 0.2 (19.4) bpm. For RESP rate estimation, SUP showed the best feasibility (98%) to obtain a reliable value from each gyroscope axis, leading to the identification of the transversal direction as the one containing the largest breathing information. These results provided evidence of the feasibility of the proposed approach with a degree of performance and feasibility dependent on the posture of the subject, under the conditions of keeping the head still, setting the grounds for future studies in real-world applications of HR and RESP rate measurement through VR headsets.

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“…SCG is a non-invasive technique for the measurement of local cardiac-induced vibrations of the chest wall that earned growing interest due to the recent availability of small and lightweight accelerometers [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. These are indeed the most common sensors used to date to acquire kinematic measurements onto the chest wall.…”

Section: Introductionmentioning

confidence: 99%

Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall

Andreozzi

Fratini

Esposito

et al. 2020

Sensors

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This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the 19th century, several techniques have been proposed to detect the mechanical vibrations caused by cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject’s chest and provides information on major events of the cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto the subject’s chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency (HF) components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency (LF) component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results of this feasibility study suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.

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Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

Cited by 23 publications

References 42 publications

Declining Trends of Heart Rate Variability According to Aging in Healthy Asian Adults

Declining Trends of Heart Rate Variability According to Aging in Healthy Asian Adults

Feasibility of Heart Rate and Respiratory Rate Estimation by Inertial Sensors Embedded in a Virtual Reality Headset

Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall

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