This paper reports on the combined analysis of seismocardiogram (SCG) and gyrocardiogram (GCG) recordings. An inertial measurement unit (IMU) consisting of a three-axis MEMS accelerometer and a three-axis MEMS gyroscope is used to record heart-induced mechanical vibrations from the chest wall of the subjects. An electrocardiogram (ECG) and an impedance cardiogram (ICG) sensor are also used as references for segmenting the cardiac cycles and recording the aortic valve opening and closure (AO and AC) events, respectively. A simplified model is proposed to explain the mechanical coupling of the chest wall to the IMU. Correlations and time differences are analyzed for the annotation of GCG and its first derivative with respect to ICG and SCG as references. Experimental results indicate a precise identification of systolic points such as the AO and AC events. The left ventricular ejection time (LVET) and pre-ejection period (PEP) metrics calculated from gyroscope recordings are also shown to accurately track their corresponding trends acquired from ICG signals. Waveform similarity analyses indicate that the first derivative of GCG has a better similarity with SCG than the GCG signal itself. Experimental results also suggest that inter-device differences in GCG recordings would need to be addressed before this technology can gain widespread application.

Objectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic. Methods: Experimental measurements were collected from twenty patients with AS and twenty healthy subjects. Firstly, a digital signal processing framework is proposed to extract time-frequency features. The features are then selected via the analysis of variance test. Different combinations of features are evaluated using the decision tree, random forest, and artificial neural network methods. Two classification tasks are conducted. The first task is a binary classification between normal subjects and AS patients. The second task is a multi-class classification of AS patients with co-existing valvular heart diseases. Results: In the binary classification task, the average accuracies achieved are 96.25% from decision tree, 97.43% from random forest, and 95.56% from neural network. The best performance is from combined SCG and GCG features with random forest classifier. In the multi-class classification, the best performance is 92.99% using the random forest classifier and SCG features. Conclusion: The results suggest that the solution could be a feasible method for classifying aortic stenosis, both in the binary and multi-class tasks. It also indicates that most of the important time-frequency features are below 11 Hz. Significance: The proposed method shows great potential to provide continuous monitoring of valvular heart diseases to prevent patients from sudden critical cardiac situations.

This work proposes a novel method of pulse transit time (PTT) measurement. The proximal arterial location data are collected from seismocardiogram (SCG) recordings by placing a micro-electromechanical accelerometer on the chest wall. The distal arterial location data are recorded using an acoustic sensor placed inside the ear. The performance of distal location recordings is evaluated by comparing SCG-acoustic and SCG-photoplethysmogram (PPG) measurements. PPG and acoustic performances under motion noise are also compared. Experimental results suggest comparable performances for the acoustic-based and PPG-based devices. The feasibility of each PTT measurement method is validated for blood pressure evaluations and its limitations are analyzed.

This paper reports on the use of the rotational component of chest vibrations for the automatic annotation of seismogram (SCG) recordings for the first time. An inertial measurement unit consisting of a three-axis MEMS accelerometer and a three-axis MEMS gyroscope is used for recording chest vibrations. The gyroscope signal acts as a reference for the detection of the isovolumic moment (IM) and aortic valve closure (AC) peaks on the SCG signal. Experimental results from ten subjects at rest indicate a close correlation between the timings of the P1 and P2 peaks of the gyroscope energy waveform and the accelerometer IM and AC peaks, and provide the capability for ECG-free annotation of the SCG signal using the energy peaks, which are simpler to detect. Furthermore, extended experiments from walking subjects reveal the feasibility of using the head-to-foot axis gyroscope recordings as an enhancement tool for the annotation of the SCG signal.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG, and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure.