Heart rate variability (HRV) is widely investigated to provide early warning signs for cardiovascular diseases (CVDs). However, traditional HRV monitoring methods are inconvenient in daily long-term continuous monitoring due to the uncomfortable direct contact of the devices with the skin. In this work, we present a ballistocardiogram (BCG)-based system for long-term HRV monitoring in a noncontact mode. BCG signals can reflect the mechanical activity of the heart, from which the beat-to-beat interval (BBI) can be extracted for HRV analysis. An effective solution is demonstrated for accurate and steady BCG signal detection using a highly sensitive fiber-optic sensor. A robust heartbeat extraction algorithm based on the deep-learning method is proposed to improve the accuracy of the BBI extraction. Continuous wavelet transform (CWT) is introduced to facilitate feature extraction against the variation of morphological characteristics of BCG signal. The accuracy of BBI estimation presents a median error of 4.4 ms, indicating the effectiveness and feasibility of the proposed system. This work is expected to pave a new and practical pathway for household HRV monitoring during sleep.

This paper presents structural characterization and performance enhancement strategies for the recently proposedA-stable and L-stable high-order integration methods based on the Obreshkov formula. It is demonstrated that although the Jacobian matrix in these methods has a bigger size than the Jacobian matrix in classical low-order methods, it enjoys a special structure that can be used to develop efficient factorization techniques. In addition, the paper proposes a method to reduce the number of Newton-Raphson iterations needed to converge in the large Jacobian domain.

This paper describes a new$A$- and$L$-stable integration method for simulating the time-domain transient response of nonlinear circuits. The proposed method, which is based on the Obreshkov formula, can be made of arbitrary high order while maintaining the$A$-stability property. The new method allows for the adoption of higher order integration methods for the transient analysis of electronic circuits while enabling them to take larger step sizes without violating the stability, leading to faster simulations. The method can be run in an$L$-stable mode to handle circuits with extremely stiff equations. Necessary theoretical foundations, implementation details, error-control mechanisms, and computational results are presented.