Low Power Bio-potential Amplifier (for EEG)

The size and dependency on power supply of current biopotential data acquisition systems prohibit continuous monitoring of biopotential signals through battery powered devices. As the interest in continuous monitoring of EEG increases for healthcare and research purposes such as seizure detection, there is an increasing need to bring down the power consumption on the biopotential amplifier (BPA). BPA is one of the most power consuming components in the biopotential data acquisition system. In this FYP, we will develop a method to improve the existing BPA using MIMOS 0.35um process technology through implementation of various low power flicker noise cancelation techniques. Techniques used include low impedance node chopping and non-overlapping demodulation chopping. The scope of this FYP is focusing on design and simulation on Cadence software in circuit level implementation. This work provides insights as well as a starting point in lowering the power consumption of bio-potential data acquisition system. This will help to enable battery power system for continuous monitoring of EEG signals in the future. This final report discusses on both the literature review, background of the projects and methodology as well as the outcome of the work. The report is concluded by suggesting future works that can be carried out in this final year project (FYP)

Cancelling Harmonic Power Line Interference in Biopotentials

Biopotential signals, like the electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and so on, contain vital information about the health state of human body. The morphology and time/frequency parameters of the biopotentials are of interest when diagnostic information is extracted and analyzed. The powerline interference (PLI), with the fundamental PLI component of 50 Hz/60 Hz and its harmonics, is one of the most disturbing noise sources in biopotential recordings that hampers the analysis of the electrical signals generated by the human body. The aim of this chapter is to review the existing methods to eliminate harmonics PLI from biopotential signals and to analyze the distortion introduced by some of the most basic approaches for PLI cancelation and whether this distortion affects the diagnostic performance in biopotentials investigations

Low Power Bio-potential Amplifier (for EEG)

The size and dependency on power supply of current biopotential data acquisition systems prohibit continuous monitoring of biopotential signals through battery powered devices. As the interest in continuous monitoring of EEG increases for healthcare and research purposes such as seizure detection, there is an increasing need to bring down the power consumption on the biopotential amplifier (BPA). BPA is one of the most power consuming components in the biopotential data acquisition system. In this FYP, we will develop a method to improve the existing BPA using MIMOS 0.35um process technology through implementation of various low power flicker noise cancelation techniques. Techniques used include low impedance node chopping and non-overlapping demodulation chopping. The scope of this FYP is focusing on design and simulation on Cadence software in circuit level implementation. This work provides insights as well as a starting point in lowering the power consumption of bio-potential data acquisition system. This will help to enable battery power system for continuous monitoring of EEG signals in the future. This final report discusses on both the literature review, background of the projects and methodology as well as the outcome of the work. The report is concluded by suggesting future works that can be carried out in this final year project (FYP)

Low-Noise Micro-Power Amplifiers for Biosignal Acquisition

There are many different types of biopotential signals, such as action potentials (APs), local field potentials (LFPs), electromyography (EMG), electrocardiogram (ECG), electroencephalogram (EEG), etc. Nerve action potentials play an important role for the analysis of human cognition, such as perception, memory, language, emotions, and motor control. EMGs provide vital information about the patients which allow clinicians to diagnose and treat many neuromuscular diseases, which could result in muscle paralysis, motor problems, etc. EEGs is critical in diagnosing epilepsy, sleep disorders, as well as brain tumors. Biopotential signals are very weak, which requires the biopotential amplifier to exhibit low input-referred noise. For example, EEGs have amplitudes from 1 μV [microvolt] to 100 μV [microvolt] with much of the energy in the sub-Hz [hertz] to 100 Hz [hertz] band. APs have amplitudes up to 500 μV [microvolt] with much of the energy in the 100 Hz [hertz] to 7 kHz [hertz] band. In wearable/implantable systems, the low-power operation of the biopotential amplifier is critical to avoid thermal damage to surrounding tissues, preserve long battery life, and enable wirelessly-delivered or harvested energy supply. For an ideal thermal-noise-limited amplifier, the amplifier power is inversely proportional to the input-referred noise of the amplifier. Therefore, there is a noise-power trade-off which must be well-balanced by the designers. In this work I propose novel amplifier topologies, which are able to significantly improve the noise-power efficiency by increasing the effective transconductance at a given current. In order to reject the DC offsets generated at the tissue-electrode interface, energy-efficient techniques are employed to create a low-frequency high-pass cutoff. The noise contribution of the high-pass cutoff circuitry is minimized by using power-efficient configurations, and optimizing the biasing and dimension of the devices. Sufficient common-mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are achieved to suppress common-mode interferences and power supply noises. Our design are fabricated in standard CMOS processes. The amplifiers’ performance are measured on the bench, and also demonstrated with biopotential recordings

Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode–skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples. The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial

Neurotechnology : design of a semi-dry electroencephalography electrode

In the research of the brain, the most complex organ of the human body, its function can be studied through the analysis of Evoked Potentials (EP). This evoked activity can be reproduced in a diverse way and recorded with an Electroencephalogram (EEG). To register the different types of brainwaves, the electrodes have a very important role. The first part of the thesis presents an extended literature review of the different types of EEG electrodes available on the market, out-standing publications and patents. A semi-dry porous ceramic electrode prototype was proposed to register EEG signals. The sensor model was developed with the aim of improving the accuracy of the actual sensors, checking many current designs, and improving artefact attenuation. It was not possible to test this design for lack of time and resources. Additionally, an EEG headset was also studied and developed to place the in-built-reservoir sensors according to the 10-20 placement system. Moreover, on the second part of this project, a skin-electrode contact impedance protocol was presented and tested with four different dry electrode materials in a diverse frequency range. The protocol used, which is a combination of some techniques already employed, has differentiated and separated the potential external hazards that can provoke an impact on bioimpedance measurements. The results obtained allow to determine the degree of utility of an electrode and how much time was required and recommended to place the electrodes before its optimal impedance acquisition.Outgoin

Towards a better understanding of the precordial leads : an engineering point of view

This thesis provides comprehensive literature review of the electrocardiography evolution to highlight the important theories behind the development of the electrocardiography device. More importantly, it discusses different electrode placement on the chest, and their clinical advantages. This work presents a technical detail of a new ECG device which was developed at MARCS institute and can record the Wilson Central Terminal (WCT) components in addition to the standard 12-lead ECG. This ECG device was used to record from 147 patients at Campbelltown hospital over three years. The first two years of recording contain 92 patients which was published in the Physionet platform under the name of Wilson Central Terminal ECG database (WCTECGdb). This novel dataset was used to demonstrate the WCT signal characterisation and investigate how WCT impacts the precordial leads. Furthermore, the clinical influence of the WCT on precordial leads in patients diagnosed with non-ST segment elevation myocardial infarction (NSTEMI) is discussed. The work presented in this research is intended to revisit some of the ECG theories and investigate the validity of them using the recorded data. Furthermore, the influence of the left leg potential on recording the precordial leads is presented, which lead to investigate whether the WCT and augmented vector foot (aVF) are proportional. Finally, a machine learning approach is proposed to minimise the Wilson Central Terminal

REDUCTION OF SKIN STRETCH INDUCED MOTION ARTIFACTS IN ELECTROCARDIOGRAM MONITORING USING ADAPTIVE FILTERING

Cardiovascular disease (CVD) is the leading cause of death in many regions worldwide, accounting for nearly one third of global deaths in 2001. Wearable electrocardiographic cardiovascular monitoring devices have contributed to reduce CVD mortality and cost by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that can be precursor to sudden cardiac death, and the long-term assessment/monitoring of symptoms, risk factors, and the effects of therapy. However, the effectiveness of ambulatory electrocardiography to improve the treatment of CVD can be significantly impaired by motion artifacts which can cause misdiagnoses, inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a main source of motion artifact in current ECG monitors. A promising approach to reduce motion artifact is the use of adaptive filtering that utilizes a measured reference input correlated with the motion artifact to extract noise from the ECG signal. Previous attempts to apply adaptive filtering to electrocardiography have employed either electrode deformation or acceleration, body acceleration, or skin/electrode impedance as a reference input, and were not successful at reducing motion artifacts in a consistent and reproducible manner. This has been essentially attributed to the lack of correlation between the reference input selected and the induced noise. In this study, motion artifacts are adaptively filtered by using skin strain as the reference signal. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The optical strain sensor is calibrated on animal skin samples and finally in-vivo, in terms of sensitivity and measurement range. Skin stretch induced artifacts are extracted in-vivo using adaptive filters. The system and method are tested for different individuals and under various types of ambulatory conditions with the noise reduction performance quantified

A new stethoscope for reduction of heart sounds from lung sound recordings.

Yip Lung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references.Abstracts in English and Chinese.Chapter 1 --- IntroductionChapter 1.1 --- Heart and Lung Diseases --- p.1Chapter 1.1.1 --- Hong Kong --- p.1Chapter 1.1.2 --- China --- p.2Chapter 1.1.3 --- the United States of America (USA) --- p.3Chapter 1.2 --- Auscultation --- p.3Chapter 1.2.1 --- Introduction of Auscultation --- p.4Chapter 1.2.2 --- Comparison between Auscultation and Ultrasound --- p.6Chapter 1.3 --- Stethoscope --- p.7Chapter 1.3.1 --- History of Stethoscope --- p.7Chapter 1.3.2 --- New Electronic Stethoscope --- p.14Chapter 1.4 --- Main Purpose of the Study --- p.16Chapter 1.5 --- Organization of Thesis --- p.16References --- p.18Chapter 2 --- A New Electronic Stethoscope's HeadChapter 2.1 --- Introduction --- p.20Chapter 2.2 --- Biopotential Electrode --- p.21Chapter 2.2.1 --- Flexible Electrode --- p.21Chapter 2.2.2 --- Laplacian Electrocardiogram --- p.22Chapter 2.3 --- Transducer --- p.25Chapter 2.4 --- Design of the Head of Stethoscope --- p.26Chapter 2.5 --- Experimental Results --- p.27Chapter 2.5.1 --- Bias Voltage of Condenser Microphone --- p.27Chapter 2.5.2 --- Frequency Response of New Stethoscope's Head --- p.29Chapter 2.6 --- Discussion --- p.30Chapter 2.7 --- Section Summary --- p.31References --- p.33Chapter 3 --- Signal Pre-processing UnitChapter 3.1 --- Introduction --- p.35Chapter 3.2 --- High Input Impedance IC Amplifier --- p.36Chapter 3.3 --- Voltage Control Voltage Source High Pass Filter Circuit --- p.37Chapter 3.4 --- Multiple Feed Back Low Pass Filter Circuit --- p.39Chapter 3.5 --- Overall Circuit --- p.41Chapter 3.6 --- Experimental Results --- p.43Chapter 3.7 --- Discussion --- p.46Chapter 3.8 --- Section Summary --- p.47References --- p.48Chapter 4 --- Central PlatformChapter 4.1 --- Introduction --- p.49Chapter 4.2 --- Adaptive Filter --- p.49Chapter 4.2.1 --- Introduction to Adaptive Filtering --- p.49Chapter 4.2.2 --- Least-Mean-Square (LMS) Algorithm --- p.51Chapter 4.2.3 --- Applications --- p.52Chapter 4.3 --- Offline Processing --- p.54Chapter 4.3.1 --- WINDAQ and MATLAB --- p.55Chapter 4.3.2 --- Direct Reference Algorithm --- p.57Chapter 4.3.3 --- Determination of Parameters in DRA --- p.62Chapter 4.3.4 --- Experimental Results of DRA --- p.67Chapter 4.3.5 --- Acoustic Waveform Based Algorithm --- p.72Chapter 4.3.6 --- Experimental Results of AWBA --- p.81Chapter 4.4 --- Online Processing --- p.85Chapter 4.4.1 --- LABVIEW --- p.85Chapter 4.4.2 --- Automated Gain Control --- p.88Chapter 4.4.3 --- Implementation of LMS adaptive filter --- p.89Chapter 4.4.4 --- Experimental Results of Online-AGC --- p.92Chapter 4.5 --- Discussion --- p.93Chapter 4.6 --- Section Summary --- p.97References --- p.98Chapter 5 --- Conclusion and Further DevelopmentChapter 5.1 --- Conclusion of the Main Contribution --- p.100Chapter 5.2 --- Future Works --- p.102Chapter 5.2.1 --- Modification of the Head of Stethoscope --- p.102Chapter 5.2.2 --- Validation of Abnormal Breath --- p.102Chapter 5.2.3 --- Low Frequency Analysis --- p.102Chapter 5.2.4 --- AGC-AWBA Approach --- p.102Chapter 5.2.5 --- Standalone Device --- p.103Chapter 5.2.6 --- Demand on Stethoscope --- p.109References --- p.110AppendixChapter A.1 --- Determination of parameters in VCVS High Pass Filter --- p.106Chapter A.2 --- Determination of parameters in MFB Low Pass Filter --- p.110Chapter A.3 --- Source code of DRA (MATLAB) --- p.114Chapter A.4 --- Source code of AWBA (MATLAB) --- p.129Chapter A.5 --- Source code of online AGC (LABVIEW) --- p.13

A Cancellation Method of Periodic Interference in Pulse-Like Signals Using an Adaptive Filter and Its Application to Flash ERGs

論文「パルス状信号に混入する交流雑音の適応フィルタによる除去法とそのフラッシュ光網膜電位図への適用」（電子情報通信学会論文誌D Vol.J94-D,No.10, pp.1685-1695. 2011 に掲載）の英訳. 許諾番号14RB002