This work introduces a heart disease classification system. The system includes electrocardiography (ECG) arrhythmia classification and phonocardiography (PCG) heart-valve diseases classification algorithm, achieving 97.4% and 99.1% accuracy. Additionally, the paper presents a procedure for lightweight convolutional neural network (CNN) model quantization with an 8-bit fix-point and 0.1% accuracy loss. Furthermore, this study proposes a programmable artificial intelligence (AI) accelerator with an application-specific instruction set processor (ASIP) and systolic array architecture to achieve high-performance computing. Moreover, we introduce a matrix mapping unit (MMU) and the pipeline state register (PSR) to facilitate switching between CNN and matrix multiplication, resulting in a reduction of over 50% in timing overhead. The chip is implemented on Xilinx’s PYNQ-Z2 and achieves a power consumption of 106 mW, with a classification latency of 6.8ms / 21ms (arrhythmia/valve diseases).

This work proposes an arrhythmia classification system. The algorithm includes naive electrocardiography (ECG) data preprocessing procedures that apply to various ECG databases. Additionally, the paper presents an ultra-lightweight model designed for arrhythmia classification, which combines a Convolutional Neural Network (CNN) with long-term heart rate information to enhance the performance of the model. The proposed model was trained and tested using the MIT-BIH and NCKU-CBIC database, following the classification standards of the Association for the Advancement of Medical Instrumentation (AAMI), achieving an accuracy of 98.5% and 97.1%. Furthermore, this work proposes a customized artificial intelligence (AI) accelerator for hardware implementation, which leverages a parallelized processing element (PE) array architecture and hybrid stationary techniques to achieve high-performance computing. The chip implementation achieves a power consumption of 122 μW, a classification latency of 6.8 ms, and an energy efficiency of 0.83 μJ/classification.

This paper proposes a voltage-controlled oscillator (VCO)-based $2^{\mathrm{n}\mathrm{d}}-$order continuous-time delta-sigma modulator (CTSDM) for current-sensing readout systems. The proposed VCO-based CTSDM can immediately quantize the current signal from sensor without pre-amplifier. A proportional-integral (PI) structure has been realized by injecting a resistor in series with the integrating capacitor to simplify the circuit complexity as well as maintain the system stability. A noise shaping with second order is implemented by the first-stage PI current integrator and a second-stage VCO phase integrator. The complementary current-steering digital-to-analog converter is adopted as the feedback path for the current subtraction. Simulation results show that the proposed current-sensing VCO-based CTSDM can achieve a signal-to-noise-and-distortion ratio (SNDR) of 81.36 dB in 10 kHz bandwidth while consuming only 13.2 $\mu$w under 1.2 V supply. This corresponds to a Figure-of-Merit (FoM) of 170.15 dB which is suitable for sensor readout applications in internet of thing (IoT).

A 3rd-order feedforward high-pass sigma-delta modulator (HPSDM) with operational amplifier (op-amp) sharing and noise-coupling techniques is presented in this paper. The modulator is suitable for biomedical signal acquisition with features of high resolution and low power consumption. Op-amp sharing technique has been utilized to reduce the number of amplifiers. To add an additional noise-shaping order, the noise-coupling technique is embedded in the summing stage without additional amplifier. To overcome the circuit sensitivity to process variation and capacitor mismatch, a new high-pass integrator structure is proposed. Simulation results reveal a Signal-to-Noise and Distortion Ratio (SNDR) of 79.64 dB consuming 1.31 $\mu$W under 1.2 V supply voltage, which can achieve peak Schreier Figure-of-Merit (FoM) of 161.64 dB and peak Walden FoM of 0.4 pJ/conv.

This paper proposed an intelligent stethoscope, which can not only visualize the heart sound signal, but also measure human's electrocardiogram (ECG) and heart sound simultaneously. With an internet of things (IoT) system and a cloud database, the stethoscope can be used in hospitals and telemedicine. The proposed stethoscope includes three parts, a front-end device for ECG and heart sound measurement, a smart device's APP and a cloud server. The ECG-measuring device is designed for single lead measurement and it has low power consumption as well as IoT-based design, which can send the real-time ECG data to the smart device's APP. On the other hand, the heart-sound-measuring device keeps the traditional stethoscope head, in order to ensure doctors can be used to the heart sound signal from this proposed stethoscope. Furthermore, it is attached an analog front-end circuit and a microphone, to filter the environmental noise and record the sound signal. At the end, the heart sound signal will be transmitted to the APP through the module of Bluetooth Low Energy (BLE). The APP on smart device can display the synchronized and real-time signals including ECG and heart sound. Meanwhile, those signals will be recorded in smart devices and uploaded to the cloud server, where doctors and users can further monitor the data. The cloud server can not only store the past signals, but also realize telemedicine through the web user interface. The proposed intelligent stethoscope has been conducted human trials in the National Cheng Kung University Hospital.

This study presents an intelligent stethoscope that can visualize heart sound signals and can simultaneously measure human's electrocardiogram (ECG) and heart sounds. The proposed stethoscope can be used in hospitals and telemedicine through an internet of things (IoT) system and a cloud database. The proposed stethoscope includes three parts, namely, a front-end device for ECG and heart sound measurement, a smart device application (APP), and a cloud server. The ECG-measuring device is designed for single lead measurement and has low power consumption and IoT-based design, which can send real-time ECG data to the smart device APP. Simultaneously, the heart-sound-measuring device combined with a traditional stethoscope head is used to measure heart sound signals. This device includes an analog front-end circuit and a microphone to filter environmental noises and to record heart sound signals. Heart sound signals are transmitted to the APP by using a Bluetooth Low Energy module. The smart device APP can display synchronized and real-time signals, including ECG and heart sounds. Meanwhile, those signals are recorded in smart devices and are uploaded to the cloud server, where doctors and users can diagnose and monitor healthcare anytime. The cloud server can store previous signals and can realize telemedicine through a web user interface. The proposed intelligent stethoscope is applied on human trials in the National Cheng Kung University Hospital.

Pulse wave velocity (PWV) is the most popular index to assessment the arterial stiffness. Currently, several non-invasive examination methods with single channel for PWV are announced. This paper proposes a non-invasive digital volume pulse (DVP) measuring system using a dual channel simultaneous measurement method try to meet the demands for home healthcare equipment which is easy to operate. Through synchronal technical in measuring DVP signals from finger and toe can achieves more precise time and substantially reduces the time spend in measurement procedures. In the other side, the proposed system developed an algorithm to locate the "foot of the wave" without facilitating of electrocardiogram (ECG).