This paper presents a systematic method for decreasing the amount of transconductors used by an operational transconductance amplifier-capacitor (OTA-C) filter to decrease the power consumption and the active area. An OTA with a local-feedback linearized technique and a transconductance booster is proposed based on the presented method. The proposed OTA combines current division with source degeneration to enhance linearity and implement low transconductance. This topology enables the proposed OTA to drive multiple integration capacitors without an additional output stage. The OTA-based circuit realizes low power consumption by operating under a weak inversion at a supply voltage of 1 V. Thus, a fifth-order ladder-type low-pass Butterworth OTA-C filter is implemented for the acquisition of electrocardiograph signals. The proposed method is validated using a prototype fabricated through a 1P6M 0.18-μm CMOS process. Results show that in ECG signal acquisition, the proposed filter has a signal bandwidth located within 250 Hz, a dynamic range of 61.2 dB, and a power consumption of 41 nW to achieve a figure-of-merit of 5.4 × 10 ^-13 . The active area of the filter is 0.24 mm ² .

This brief proposes a fifth-order Butterworth operational transconductance amplifier-C (OTA-C) low-pass filter (LPF) with multiple-output differential-input (MODI) OTA structure and metal-insulator-metal capacitors for electrocardiography applications. The current division technology is used as an alternative output pair to provide multiple outputs and achieve high linearity. This technique reduces the number of OTAs of the fifth-order LPF from 11 to 6 as compared with the conventional structure. The design issue of linearity is also considered in the implementation of MODI OTA. The proposed filter is fabricated in a 0.18 μm complementary metal-oxide- semiconductor technology with an area of 0.12 mm2. The LPF achieved a total harmonic distortion of 49.8 dBc under a bandwidth of 50 Hz and input voltage of 86 mV _pp at a 1-V supply voltage. The total power dissipation is 350 nW.

Epilepsy is a common clinical disease. Severe epilepsy can be life-threatening in certain unexpected conditions, so it is important to detect seizures instantly with a wearable device and to provide treatment within the golden window. The observation of the electroencephalography (EEG) signal is an imperative method to assist correct epilepsy diagnosis. To detect and classify EEG signals, a convolutional neural network (CNN) is an intuitive and appropriate method that borrows expertise from neurologists. However, the computational cost of training and inference on artificial intelligence (AI)-based solutions make software-only and hardware-only solutions incompetent for real-time monitoring on embedded devices. Hence, this study proposes three key contributions for the challenge, namely, an algorithm framework to provide real-time epilepsy detection, a dedicated coprocessor chip implementing this framework to enable real time epilepsy detection to offload and accelerate detection algorithm, and a custom interface with the coprocessor and reduced instruction set computer-V (RISC-V) instructions to reconfigure the coprocessor and transfer data. The epilepsy detection framework is implemented in 11-layer CNN. The proposed epilepsy detection algorithm performs 97.8% accuracy for floating-point and 93.5% for fixed-point operations through animal experiments with lab rats. The RISC-V CNN coprocessor is fabricated in the TSMC 0.18-μm CMOS process. For each classification, the coprocessor consumes 51 nJ/class. and 0.9 µJ/class. energy on data transfer and inference, respectively. The detection latency on the chip is 0.012 s. With the integration of the hardware coprocessor, AI algorithms can be applied to epilepsy detection for real-time monitoring.

In patients with diabetes mellitus (DM), incident cardiovascular (CV) events are associated with poor long-term outcomes. Serum high-sensitivity troponin I (hs-TnI) is widely used to diagnose and predict outcomes in patients with acute coronary syndrome, however, few studies have investigated the accuracy of urine hs-TnI as a predictor for incident CV events in patients with DM. The enrolled participants included patients with DM. Fresh urine hs-TnI levels were measured. Medical records of enrolled patients were used to determine the number of incident CV events prospectively for 3 months. The study cohort comprised 378 participants. We observed significantly higher levels of urine hs-TnI in those with than without subsequent incident CV events. The multivariate logistic regression analysis using different models consistently showed that urine hs-TnI > 4.10 pg/mL was an independent factor predictive of incident CV events. The ROC-AUC analysis revealed that the optimal cutoff value for urine hs-TnI for predicting incident CV events was 1.55 pg/mL and the area was 0.611 (p = 0.027). A single measurement of urinary hs-TnI, collected easily and non-invasively, may be an acceptable biomarker for predicting subsequent incident CV events in patients with DM.

Electrocardiography (ECG) is a fundamental method not only commonly used in the hospital for clinical requirement but also widely adopted in home and personal healthcare systems to obtain the electrical activity of the heart. An arrhythmia monitoring system is proposed and used in a clinical trial. The proposed system has three parts. The first is a high-resolution, low-power analog front-end circuit for implementing bio-signal sensing circuits. This part is developed with a chopper-based pre-amplifier and a high-pass sigma-delta modulator. The features of the circuits are low complexity, high resolution, and low power consumption. The second part is a digital signal processor with a decimation filter and a universal asynchronous receiver/transmitter package generator. The last part is used to realize a software interface on smartphone for ECG signal recording, display, and classification. A wavelet-based classification method is also proposed to classify the rhythm. The chip used in the system is fabricated through the 0.18 ?m standard complementary metal-oxide-semiconductor process, and the operation voltage is 1.2 V. The classification algorithm is verified with data from the MIT/BIH Arrhythmia Database. The accuracy of beat detection and arrhythmia classification is 99.4% and 95.83%, respectively. Eight patients are enrolled in a human study to verify the performance of the proposed arrhythmia monitoring system. Results show that the system can acquire and classify ECG signals.

Heart-sound auscultation is a rapid and fundamental technique used for examining the cardiovascular system. The main components of heart sounds are the first and second heart sounds. Discriminating these heart sounds under the presence of additional heart sounds and murmurs will be difficult. To recognize these signals efficiently, this study proposes a monitoring system with phonocardiogram and electrocardiogram. This system has two key points. The first is chip implementation, including capacitor coupled amplifier, transimpedance amplifier, high-pass sigma-delta modulator, and digital signal processing block. The chip in the system is fabricated in 0.18 μm standard complementary metal-oxide-semiconductor process. The second is a software application on smartphones for heart-related physiological signal recording, display, and identification. A wavelet-based QRS complex detection algorithm verified by MIT/BIH Arrhythmia Database is also proposed. The overall measured positive prediction, sensitivity, and error rate of the proposed algorithm are 99.90%, 99.82%, and 0.28%, respectively. During auscultation, doctors may refer to these physiological signals displayed on the smartphone and simultaneously listen to the heart sounds to diagnose the potential heart disease. By taking advantage of signal visualization and keeping the original diagnosis procedure, the uncertainty existing in heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.