SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine.

This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from different body areas. A custom low-power transmission protocol was developed to allow the concomitant real-time monitoring of 32 signals (16 bit @200 Hz) from up to 12 nodes with a jitter in the among-node time synchronization lower than 0.2 ms. The BluetoothLE protocol may be used when only a single node is needed. Data can also be collected in the off-line mode. Seismocardiogram and pulse transit times can be derived from the collected data to obtain additional information on cardiac mechanics and vascular characteristics. The employment of the system in the field showed recordings without data gaps caused by transmission errors, and the duration of each battery charge exceeded 16 h. The system is currently used to investigate strategies of hemodynamic regulation in different vascular districts (through a multisite assessment of ECG and PPG) and to study the propagation of precordial vibrations along the thorax. The single-node version is presently exploited to monitor cardiac patients during telerehabilitation.

Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation.

Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For estimation of the pre-ejection period (PEP), 43% of ICG annotations fell in the corresponding echocardiography ranges while this was 86% for SCG. For estimation of the total systolic time (TST), these numbers were 43, 80, and 90% for ICG, PCG, and SCG, respectively. In summary, SCG and PCG signals provided an acceptable accuracy and precision in estimating cardiac timings, as compared to ICG.

An algorithm for the beat-to-beat assessment of cardiac mechanics during sleep on Earth and in microgravity from the seismocardiogram.

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.

Use of seismocardiogram for the beat-to-beat assessment of the Pulse Transit Time: A pilot study.

We propose a new methodology for the estimation of Pulse Transit Time, PTT, based on the use of the seismocardiogram for the identification of the aortic valve opening, AO. This method has been implemented to obtain a first description of the AO-derived PTT beat-to-beat variability at rest and during the recovery after a cycloergometer exercise at 25W and 100W, its relation with systolic blood pressure, S(BP), and its difference with respect to variability of the Pulse Arrival Time, PAT (i.e. the BP transit time estimated by considering the ECG R peak instead of AO as proximal site). Our preliminary data indicate that 1) the fast components of the PTT variability are only marginally influenced by respiration; 2) only the slower components of the PTT variability are correlated with systolic BP; 3) major differences exist in the dynamics of PTT and PAT, being PAT variability significantly larger and importantly influenced by the beat-to-beat changes occurring in the Pre Ejection Period.

Wearable seismocardiography for the beat-to-beat assessment of cardiac intervals during sleep.

Seismocardiogram (SCG) can be detected during sleep by a textile-based wearable system. This pilot study preliminarily explores the feasibility of a beat-to-beat estimation of cardiac mechanical features (RR interval, RRI, Pre-Ejection Period, PEP, Isovolumic Contraction Time, ICT, Left Ventricular Ejection Time, LVET, Isovolumic Relaxation Time, IRT) from the joint ECG and SCG assessment during sleep. The analysis of two 30-min sleep data segments from one healthy subject, indicated that 1) respiration largely influence the dynamics of most of the parameters; 2) variability of cardiac intervals is only marginally influenced by the RRI variability; 3) appreciable spectral power at frequencies ≤ 0.1 is only observed in the RRI spectrum and not in the spectra of the other indexes; 4) IRT has a broadband variability, that is clearly different from the dynamics of the other indexes. These findings represent the very first description of the beat-to-beat variability of cardiac mechanical indexes. Further investigations on a larger population are in progress to confirm the present results.

A textile-based wearable system for the prolonged assessment of cardiac mechanics in daily life.

Seismocardiogram, SCG, can be detected over the 24 hours in ambulant subjects by a textile-based wearable system together with the electrocardiogram, ECG and respiration. In this pilot study we explored the possibility to derive 24 h profiles of cardiac time intervals, i.e. indexes of heart mechanical function, from the SCG recordings performed in daily life conditions by the above wearable system. Two healthy subjects were recruited for the study. They worn the system for 24 hours during a working day. From each recording, every 30 minutes the following parameters were derived from the ECG and SCG signals: RR interval, RRI, Pre-Ejection Period, PEP, Isovolumic Contraction Time, ICT, Left Ventricular Ejection Time, LVET, Isovolumic Relaxation Time, IRT. From the analysis it appears that 1) all parameters are characterized by a coefficient of variation in the same order of magnitude, and 2) 24 h LVET time profiles mirrors the long term RRI behavior. Common trends in PEP and ICT profiles were observed in one subject. This study indicates that indexes of cardiac mechanics can be derived from SCG recordings performed over the 24 hours. The obtained positive results encourage further studies to refine this methodology and confirm the present findings.