Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces

Multi-Biosignal Sensing Interface with Direct Sleep-Stage Classification

Department of Electrical EngineeringSleep is a time of mental and physical rest in a person???s daily cycle. It is an indispensable metabolic activity that helps the body grow and boosts immunity. Therefore, sleep disorders can cause illness in the body as well as just physical condition. Among these diseases are typically included rapid eye movement (REM) sleep behavior disorder, nocturnal enuresis, sleepwalking, etc., which can cause serious injury on sleep. Sleep disorders are a common disease. According to a survey, sleep deprivation and disability affect a significant part of the world???s population. It is a disease that affects tens of millions of people around the world. In general, treatment for sleep disorders checks the quality during sleep and prescribes various sleep diseases by checking the condition of sleep. Sleep quality and sleep disease are determined by the depth and time of the sleep phase. Therefore, the analysis and classification of the sleep stage are essential. According to the manual of the American Academy of Sleep Medicine (AASM), sleep stages are divided into five stages. Various methods for sleep analysis have been developed. Polysomnography (PSG), called the golden standard, is the most reliable measurement of sleep quality in hospitals for sleep disorders, but this conventional method requires the use of various human body signals, and it is difficult to access due to the complex interface and various electrodes. It is not economical because of its infrastructure, which does not lead to direct treatment of prospective patients. In addition, the conventional interface system process is not an integrated interface system. The integrated interface system refers to the integration of the interface in the measuring and analysis process. Conventional sensing and analysis take place on the instrument measuring the patient and on the analyst???s computer. Therefore, conventional treatments are not economical and make patient self-analysis difficult. Furthermore, this makes it difficult to increase the demand for prospective patients. This paper presents a multi-biosignal sensing interface system with direct sleep-stage classification. Unlike conventional systems, this work proposes an interface system that is an integrated interface system, measuring, and analysis based on the analog circuit and system. The proposed paper configures a multi-biosignal sensing interface consisting of single-channel EEG, EMG, and 2EoG. The multi-biosignal sensing readout integrated circuit (ROIC) collects analog signals from the electrodes and extracts features from the signal. The multi-biosignal sensing ROIC has a feature extraction stage that directly extracts the characteristic of sleep stages. The analog feature extraction stage consists of the optimized circuit for three multi-biosignal extracts the feature of each stage during sleep on the waveform. The extracted signal is scored by the rule-based decision tree sleep stage proposed by the micro controller unit (MCU). The multi-biosignal sensing ROIC can analyze the sleep stage through EEG, EMG, and 2EOG, and can simultaneously analyze four channels. The multi-biosignal sensing ROIC is implemented using a compensate metal-oxide-semiconductor 0.18um process. In addition, this system implements a low-power, integrated module for portable device configuration, and from this interface makes a smart headband for prospective patients. Depending on the purpose of use, it consists of 2 type paths, including raw data recording and analog feature extraction based direct sleep classification using decision tree algorithm. Finally, sleep stage scoring can be displayed, or raw data can be sent to the personal computer interface to increase accuracy. The sleep stage was verified by comparing the OpenBCI module-based MATLAB analysis using SVM with this system, and the result shows an overall accuracy of 74% for four sleep stages.clos

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 Multi-Sensor Platform for Microcurrent Skin Stimulation during Slow Wave Sleep

Insu cient and low quality sleep is related to several health issues and social outcomes. Regular sleep study conducted in a sleep laboratory is impractical and expensive. As a result, miniature and non-invasive sleep monitoring devices provide an accessible sleep data. Though not as accurate as polysomnography, these devices provide useful data to the subject by tracking sleep patterns regularly. On the other hand, proactive improvement of sleep quality has been limited to pharmacological solutions and cranial electrotherapy stimulation. An alternative approach and a potential solution to sleep deprivation is a non-pharmacological technique which involves the application of micro-current electrical stimulation on the palm during Slow Wave Sleep (SWS). This thesis presents the development of a miniature device for SWS detection and electrocutaneous stimulation. Several sensors are embedded in the prototype device to measure physiological data such as body movement, electrodermal activity, heart rate, and skin and ambient temperature. Furthermore, the prototype device provides local storage and wireless transfer for data acquisition. The quality of the sensor data during sleep are discussed in this thesis. For future work, the results of this thesis shall be the used as a baseline to develop a more re ned prototype for clinical trials in sleep laboratories

Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces