Objective: Traditional definition of sample entropy (SampEn), here referred to as global SampEn (GSampEn), provides a conditional entropy estimate that blurs the local statistical properties of the time series. We hypothesized that a local version of SampEn (LSampEn) might be more powerful in the presence of determinism than GSampEn. Methods: LSampEn was computed by calculating the probability of the current sample conditioned on each reference pattern and averaging it over all reference patterns. The improved ability of LSampEn compared to GSampEn was demonstrated by simulating deterministic periodic, deterministic chaotic, and linear stochastic dynamics corrupted by additive noise and over real cardiovascular variability series recorded from 16 healthy subjects (max-min age range: 22-58 years) during incremental bicycle ergometer exercise. Results: We found that: i) LSampEn is more robust in describing deterministic periodic or nonlinear features in the presence of additive noise than GSampEn, ii) in association with a surrogate approach, LSampEn is more powerful in detecting nonlinear dynamics than GSampEn, iii) LSampEn and GSampEn are equivalent in the presence of stochastic linear dynamics, and iv) only LSampEn can detect the decrease of complexity of heart period variability during bicycle exercise being a likely hallmark of sympathetic activation. Conclusion: LSampEn preserves the GSampEn capability in characterizing the complexity of short sequences but improves its reliability in the presence of deterministic patterns featuring sharp state transitions and nonlinear dynamics. Significance: Variations of complexity can be measured with a greater statistical power over short series using LSampEn, especially when nonlinear features are present.

Seismocardiogram (SCG) is the recording of the minute body accelerations induced by the heart activity, and reflects mechanical aspects of heart contraction and blood ejection. So far, most of the available systems for the SCG assessment are designed to be used in a laboratory or in controlled behavioral and environmental conditions. In this paper we propose a modified version of a textile-based wearable device for the unobtrusive recording of ECG, respiration and accelerometric data (the MagIC system), to assess the 3d sternal SCG in daily life. SCG is characterized by an extremely low magnitude of the accelerations (in the order of g × 10 ^-3 ), and is masked by major body accelerations induced by locomotion. Thus in daily life recordings, SCG can be measured whenever the subject is still. We observed that about 30 seconds of motionless behavior are sufficient for a stable estimate of the average SCG waveform, independently from the subject's posture. Since it is likely that during spontaneous behavior the subject may stay still for at least 30 seconds several times in a day, it is expected that the SCG could be repeatedly estimated and tracked over time through a prolonged data recording. These observations represent the first testing of the system in the assessment of SCG out of a laboratory environment, and open the possibility to perform SCG studies in a wide range of everyday conditions without interfering with the subject's activity tasks.

Seismocardiogram, SCG, is the measure of precordial vibrations produced by the beating heart, from which cardiac mechanics may be explored on a beat-to-beat basis. We recently collected a large amount of SCG data (>69 recording hours) from an astronaut to investigate cardiac mechanics during sleep aboard the International Space Station and on Earth. SCG sleep recordings are characterized by a prolonged duration and wide heart rate swings, thus a specific algorithm was developed for their analysis. In this article we describe the new algorithm and its performance. The algorithm is composed of three parts: 1) artifacts removal, 2) identification in each SCG waveform of four fiducial points associated with the opening and closure of the aortic and mitral valves, 3) beat-to-beat computation of indexes of cardiac mechanics from the SCG fiducial points. The algorithm was tested on two sleep recordings and yielded the identification of the fiducial points in more than 36,000 beats with a precision, quantified by the Positive Predictive Value, ≥99.2%. These positive findings provide the first evidence that cardiac mechanics may be explored by the automatic analysis of SCG long-lasting recordings, taken out of the laboratory setting, and in presence of significant heart rate modulations.

Seismocardiography is the body-surface recording of vibrations produced by the beating heart. A high frequency (HF) accelerometric component of the seismocardiogram (SCG) is related to the heart sounds generated by the closure of atrio-ventricular and semilunar valves. This paper evaluates the feasibility of recording the SCG component associated to cardiac sounds by means of a wearable device originally designed for monitoring ECG, respiratory movements, body accelerations and posture in freely moving subjects. The method is based on the averaging of the HF component of the acceleration vector measured by the wearable system, and on the subsequent extraction of features from its envelope. The method is applied on data recorded in healthy volunteers in different postures and during sleep. Results indicate that it is possible to reliably identify the time of occurrence of the first and second heart sound within the cardiac cycle. They also show significant differences in the HF component of SCG between supine and standing postures. Analyzing the HF SCG in a volunteer sleeping at high altitude (4554 m asl) substantial differences were also found among three body positions (lying supine or on the left or right side). These differences are likely to reflect changes in cardiac mechanics induced by different postures of the body.

Seismocardiogram (SCG) is the measure of the minute vibrations produced by the beating heart. We previously demonstrated that SCG, ECG and respiration could be recorded over the 24h during spontaneous behavior by a smart garment, the MagIC-SCG system. In the present case study we explored the feasibility of a beat-to-beat estimation of two indices of heart contractility, the Left Ventricular Ejection Time (LVET) and the electromechanical systole (QS2) from SCG and ECG recordings obtained by the MagIC-SCG device in one subject. We considered data collected during outdoor spontaneous behavior (while sitting in the metro and in the office) and in a laboratory setting (in supine and sitting posture, and during recovery after 100W and 140W cycling). LVET was estimated from SCG as the time interval between the opening and closure of the aortic valve, QS2 as the time interval between the Q wave of the ECG and the closure of the aortic valve. In every condition, LVET and QS2 could be estimated on a beat-to-beat basis from the SCG collected by the smart garment. LVET and QS2 are characterized by important beat-to-beat fluctuations, with standard deviations in the same order of magnitude of RR Interval. In all settings, spectral profiles are different for LVET, QS2 and RR Interval. This suggests that the biological mechanisms impinging on the heart exert a differentiated influence on the variability of each of these three indices.