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 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)

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

Digitally-assisted, ultra-low power circuits and systems for medical applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-225).In recent years, trends in the medical industry have created a growing demand for a variety of implantable medical devices. At the same time, advances in integrated circuits techniques, particularly in CMOS, have opened possibilities for advanced implantable systems that are very small and consume minimal energy. Minimizing the volume of medical implants is important as it allows for less invasive procedures and greater comfort to patients. Minimizing energy consumption is imperative as batteries must last at least a decade without replacement. Two primary functions that consume energy in medical implants are sensor interfaces that collect information from biomedical signals, and radios that allow the implant to communicate with a base-station outside of the body. The general focus of this work was the development of circuits and systems that minimize the size and energy required to carry out these two functions. The first part of this work focuses on laying down the theoretical framework for an ultra-low power radio, including advances to the literature in the area of super-regeneration. The second part includes the design of a transceiver optimized for medical implants, and its implementation in a CMOS process. The final part describes the design of a sensor interface that leverages novel analog and digital techniques to reduce the system's size and improve its functionality. This final part was developed in conjunction with Marcus Yip.by Jose L. Bohorquez.Ph.D