Xingchen Xu

This paper presents a high-resolution analog front-end (AFE) circuit. The AFE integrates a low-noise current amplifier and a second-order delta-sigma modulator (DSM). Implemented in 22nm Fully Depleted Silicon-On-Insulator (FD-SOI) technology, the amplifier achieves a bandwidth of 16MHz with gain up to MΩ-range at 10 kHz. The measured input current noise densities reach 38.56 fA/√Hz at 1 Hz and 21.28 fA/√Hz at 10 Hz, while maintaining a common-mode rejection ratio (CMRR) of 162 dB. The DSM operates under 1.2 V supply voltage with 12.8 MHz sampling frequency, demonstrating 91.3 dB SNDR, 13.9-bit ENOB, and 160.44 dB FoMSNDR at 3.66 mW power consumption. Benefiting from the noise suppression and interference immunity of FD-SOI technology, the proposed design demonstrates low-noise characteristics, low-power consumption, and an enhanced CMRR. It addresses engineering implementation pain points such as "high noise interference, high power consumption, and insufficient accuracy" in weak current signal detection, and provides a highly reliable hardware solution for signal acquisition needs in fields including biomedical applications, industrial sensing, and precision instruments.

This article presents a 6.78 MHz single-stage wireless power transfer receiver (RX) using 0X/1X regulating rectifier with improved voltage mode (VM) delay compensation for implantable medical devices (IMDs). The regulation of 0X/1X modes is achieved through pulse width modulation (PWM), with the frequency set at 1/16 of the carrier clock to ensure circuit stability and enhance power conversion efficiency (PCE) under light loads. VM delay compensation has the advantage of power consumption. However, the traditional VM on-delay compensation may become ineffective because a large voltage pulse will occur on the RX side input when the circuit delays increase. Therefore, an improved VM on-delay compensation method is proposed. Coarse compensation for on-delays can be achieved by reusing the control signal generated from off-delay compensation loop, as off-delay compensation processes will not be influenced by on-delay compensation processes. It allows the on-delay compensation loop to function normally and finely compensate for on-delays. The proposed rectifier is designed in 0.18μm CMOS process. It can achieve output voltage management of 1.8V. Comparing with other regulating rectifier, the proposed rectifier has a fast transient response of 14 μs and a small chip area of 0.91 mm ² . The simulation results indicate that this rectifier can attain high PCE of 84.6% in light load (500Ω) and peak PCE of 93.5% in heavy load (50Ω). The high voltage conversion ratio (VCR) of over 93% is also achieved under the large circuit delays.

Abnormalities in cardiac function arise irregularly and typically involve multimodal electrical, mechanical vibrations, and acoustics alterations. This paper proposes an Electro-Mechano-Acoustic (EMA) activity model for mapping the complete macroscopic cardiac function to refine the systematic interpretation of cardiac multimodal assessment. We abstract this activity pattern and build the mapping system by analyzing the functional comparison of the heart pump and Electronic Fuel Injection (EFI) system from the multimodal characteristics of the heart. Electrocardiogram (ECG), seismocardiogram (SCG) & Ultra-Low Frequency seismocardiogram (ULF-SCG), and Phonocardiogram (PCG) are selected to implement the EMA mapping respectively. First, a novel low-frequency cardiograph compound sensor capable of extracting both SCG and ULF-SCG is proposed, which is integrated with ECG and PCG modules on a single hardware device for portable dynamic acquisition. Afterward, a multimodal signal processing chain further analyses the acquired synchronized signals, and the extracted ULF-SCG is shown to indicate changes in heart volume. In particular, the proposed method based on waveform curvature is used to extract 9 feature points of the SCG signal, and the overall recognition accuracy reaches over 90% in the data collected by EMA portable device. Ultimately, we integrate the portable device and signal processing chains to form the EMA cardiovascular mapping system (EMACMS). As a next-generation system solution for cardiac daily dynamic monitoring, which can map the mechanical coupling and electromechanical coupling process, extract multi-characteristic heart rate variability (HRV), and enable extraction of important time intervals of cardiac activity to assess cardiac function.

The accurate localization of S1 and S2 is essential for heart sound segmentation and classification. However, current direct heart sound segmentation algorithms have poor noise immunity and low accuracy. Therefore, this paper proposes a new optimal heart sound segmentation algorithm based on K-means clustering and Haar wavelet transform. The algorithm includes three parts. Firstly, this method uses the Viola integral method and Shannon’s energy-based algorithm to extract the function of the envelope of the heart sound energy. Secondly, the time–frequency domain features of the acquired envelope are extracted from different dimensions and the optimal peak is searched adaptively based on a dynamic segmentation threshold. Finally, K-means clustering and Haar wavelet transform are implemented to localize S1 and S2 of heart sounds in the time domain. After validation, the recognition rate of S1 reached 98.02% and that of S2 reached 96.76%. The model outperforms other effective methods that have been implemented. The algorithm has high robustness and noise immunity. Therefore, it can provide a new method for feature extraction and analysis of heart sound signals collected in clinical settings.