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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. [ABSTRACT FROM AUTHOR]

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security. [ABSTRACT FROM AUTHOR]

In this paper, we describe and test a new portable device that is able to deliver tactile interoceptive stimulation. The device works by delivering precise interoceptive parasympathetic stimuli to C-tactile afferents connected to the lamina I spinothalamocortical system. In humans, interoceptive stimulation can be used to enhance heart rate variability (HRV). To test the effectiveness of the device in enhancing HRV, 13 subjects were randomly assigned in a single-blind between-subjects design either to the experimental condition or to the control condition. In the experimental condition, subjects received stimulation with the developed device; in the control condition subjects received stimulation with static non-interoceptive pressure. Subjects’ electrocardiograms (ECG) were recorded, with sampling at 1000 Hz for 5 min as a baseline, and then during the stimulations (11 min). Time domain analyses were performed to estimate the short-term vagally mediated component (rMSSD) of HRV. Results indicated that the experimental group showed enhanced rMSSD, compared to the control group. Moreover, frequency domain analyses indicated that high frequency band power, which reflects parasympathetic activity in humans, also appeared to be enhanced in the experimental group compared to control subjects. Conclusions and future challenges for an embodied perspective of rehabilitative medicine are discussed. [ABSTRACT FROM AUTHOR]