Wearable Multi-Biosignal Analysis Integrated Interface with Direct Sleep-Stage Classification

This paper presents a wearable multi-biosignal wireless interface for sleep analysis. It enables comfortable sleep monitoring with direct sleep-stage classification capability while conventional analytic interfaces including the Polysomnography (PSG) require complex post-processing analyses based on heavy raw data, need expert supervision for measurements, or do not provide comfortable fit for long-time wearing. The proposed multi-biosignal interface consists of electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG). A readout integrated circuit (ROIC) is designed to collect three kinds of bio-potential signals through four internal readout channels, where their analog feature extraction circuits are included together to provide sleep-stage classification directly. The designed multi-biosignal sensing ROIC is fabricated in a 180-nm complementary metal & x2013;oxide & x2013;semiconductor (CMOS) process. For system-level verification, its low-power headband-style analytic device is implemented for wearable sleep monitoring, where the direct sleep-stage classification is performed based on a decision tree algorithm. It is functionally verified by comparison experiments with post-processing analysis results from the OpenBCI module, whose sleep-stage detection shows reasonable correlation of 74% for four sleep stages

A wireless ExG interface for patch-type ECG holter and EMG-controlled robot hand

This paper presents a wearable electrophysiological interface with enhanced immunity to motion artifacts. Anti-artifact schemes, including a patch-type modular structure and real-time automatic level adjustment, are proposed and verified in two wireless system prototypes of a patch-type electrocardiogram (ECG) module and an electromyogram (EMG)-based robot-hand controller. Their common ExG readout integrated circuit (ROIC), which is reconfigurable for multiple physiological interfaces, is designed and fabricated in a 0.18 ??m CMOS process. Moreover, analog pre-processing structures based on envelope detection are integrated with one another to mitigate signal processing burdens in the digital domain effectivel

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Department of Electrical Engineeringope

A Stepwise Split Power-Driving Scheme With Automatic Slope Control for EMC-Enhanced LIN Transceiver

This paper proposes a split power-driving scheme for electromagnetic interference robust design and its automatic stepwise digital slope control for suppression of spectral emission. This analog-digital hybrid method gives power saving effect up to 24.4%, and also production reliability can be improved by utilizing a failure detection and recovery scheme. For feasibility of the proposed electromagnetic compatibility (EMC) scheme, a Local Interconnect Network transceiver prototype is fabricated in a 0.18-??m BCD process, and experimentally verified to provide EMC-enhanced operation with low-cost and low-power overheads

A Wearable Integrated Bio-signal sensing interface Systems for Polysomnography

Polysomnography(PSG) is golden standard that analysis and diagnose for sleep status. A polysomnogram will typically record a minimum of 12 channels requiring a minimum of 22 wire attachments to the patient. So, it is difficult for an individual to utilize. This paper presents the prototype of a wearable integrated bio-signal sensing interface systems for PSG. The system consists of 5 channel and feature extraction circuit to sense a various body signal, such as EEG,EMG, and EoG. Amplified analog signal goes to feature extraction block not software processing but scheme to detect the feature extraction of EEG in frequency domain. In order to detect the low frequency band like EEG, we implemented to Teager Energy Operator with normalize using gm cell[1]. The implemented ROIC was fabricated with 1.8V CMOS technology and it is implemented to dual-mode system for monitoring and diagnose

Hybrid hardware-algorithm platform for EMG pattern recognition

The effort of making an wearable sensor module, which can sense, collect and upload electrophysiology data such as electromyogram(EMG), electrocardiogram(ECG) and electroencephalogram(EEG), is keep increasing in the research area. At the same time, faster computer gives the occasion to apply and deploy machine learning algorithm (M.L) to the healthcare application. These two phenomena met together and opened a new era of the signal processing for electrophysiological data. One of the branch on the application of M.L with the EMG is to recognize patterns (EMG-PR) of human gestures. Many studies have been done to augment the accuracy of the EMG-PR. For example, there is a way to analyze and to approach with the mathematical way, a method to compare EMG-PR method, a study mixing different EMG-PR method to increase the accuracy and research applying 1 M.L technic after doing a data preprocessing. The data preprocessing is a common step for the EMG-PR and wavelet transform was proved for its efficiency. In this paper we present a programmable ASIC chip including 3 channels of EMG detectable circuits, a wavelet filter and an ADC with the resolution varying from 8 to 12 bits. EMG-PR study is generally limited in the hardware part and various studies were done using a commercial hardware material such as EMG Myo-Armband to get the EMG raw data. Here we designed an efficient ASIC chip with 3 channels including an algorithm processing. In comparison with the commercial Myo armband, the power consumption of the platform including the STM32F0 series microcontroller, Bluetooth, and the ASIC chip, is lower. The communicated data would then already be wavelet-processed data so the computational time for the data pre-processing could decrease for the input of the BP ANN

Sensor Interface for Electromyogram with Wavelet Filter

This paper presents electromyogram(EMG) sensing interface with wavelet process. Wavelet process is effective to analyze EMG signal because it provides signal information in frequency domain. The power of EMG signal is concentrated in 50~150Hz range. By using wavelet transform filters, the EMG signal is decomposed in several ranges of frequency. In this process, immunity to motion artifacts is enhanced and analyzing an EMG with various frequency ranges gives more information about specific movement. the amount of data is decreased because down sampling is used in wavelet transform process. Thus, it improves communication efficiency. The proposed interface is designed in 0.18??m CMOS process

Bio-impedance Instrumentation system with base resistance cancellation based on DC servo loop

Body impedance modality is an emerging technique to sense and analyze body signal like respiration and body fat. In this paper, by implementing base resistance cancellation blocks in body impedance instrumentation amplifier and system, it can achieve to sense respiration signal with more power efficiency and resolution

A Supply-Scalable Dual-Rate Dual-Mode DAC With an Adaptive Swing Control

This brief presents a dual-rate dual-mode power digital-to-analog converter (DAC) circuit to cover wide range of supply voltages. For various applications, it is designed to have dual conversion rate of Nyquist and oversampling in conventional current-steering DACs, and its output stage is implemented to support two kind modes of high-voltage and low-voltage. To achieve optimal performance of spurious-free dynamic range, the output swing is adaptively maximized depending on supply voltage levels. For feasibility of the proposed supply-scalable dual-rate dual-mode structure, a power DAC prototype is fabricated in a 0.18 mm bipolar CMOS DMOS process, and experimentally verified to provide normalized power efficiency of 81.5%

A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing

This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18-??m complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time hand-gesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 % with a real-time frequency analysis