High-performance analog front-end (AFE) for EOG systems

Electrooculography is a technique for measuring the corneo-retinal standing potential of the human eye. The resulting signal is called the electrooculogram (EOG). The primary applications are in ophthalmological diagnosis and in recording eye movements to develop simple human–machine interfaces (HCI). The electronic circuits for EOG signal conditioning are well known in the field of electronic instrumentation; however, the specific characteristics of the EOG signal make a careful electronic design necessary. This work is devoted to presenting the most important issues related to the design of an EOG analog front-end (AFE). In this respect, it is essential to analyze the possible sources of noise, interference, and motion artifacts and how to minimize their effects. Considering these issues, the complete design of an AFE for EOG systems is reported in this work.info:eu-repo/semantics/publishedVersio

Advanced Interfaces for HMI in Hand Gesture Recognition

The present thesis investigates techniques and technologies for high quality Human Machine Interfaces (HMI) in biomedical applications. Starting from a literature review and considering market SoA in this field, the thesis explores advanced sensor interfaces, wearable computing and machine learning techniques for embedded resource-constrained systems. The research starts from the design and implementation of a real-time control system for a multifinger hand prosthesis based on pattern recognition algorithms. This system is capable to control an artificial hand using a natural gesture interface, considering the challenges related to the trade-off between responsiveness, accuracy and light computation. Furthermore, the thesis addresses the challenges related to the design of a scalable and versatile system for gesture recognition with the integration of a novel sensor interface for wearable medical and consumer application

Power Line Noise Suppression Using N-path Notch Filter in ECG Signal Acquisition

芝浦工業大学2020年

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Firmware design of a portable medical device to measure the quadriceps muscle group after a total knee arthroplasty by EMG, LBIA and clinical score methods

El objetivo de este proyecto es el diseño del firmware de un dispositivo médico portátil para mediciones de EMG y LBIA, que se utilizará para la evaluación de pacientes de artroplastia total de rodilla, para estudiar la progresión de diferentes prótesis de rodilla (Medial-Pivot y Ultra-Congruente). En la tesis, se expone el conocimiento actual de los estudios y aplicaciones de EMG y LBIA, junto con los dispositivos comerciales utilizados actualmente. Además, se han estudiado e implementado las diferentes técnicas de filtrado y procesamiento digital para señales de EMG y LBIAs. Adicionalmente, se ha realizado un estudio estadístico preliminar con datos LBIA de 12 pacientes de artroplastia total de rodilla. El diseño del firmware de esta tesis incluye: los procesos de adquisición de datos con el uso de diferentes ADCs (Conversor Analógico a Digital) (de la propia placa y externos, utilizando la interfaz SPI) y un DAC (Conversor Digital a Analógico), el correspondiente procesamiento de la señal y la extracción de sus características, la comunicación con un dispositivo externo utilizando un módulo BLE externo con interfaz UART, el proceso de encriptación de los datos médicos, la funcionalidad de manejo de errores y la aproximación del nivel de batería. En esta tesis, todos los flujos de trabajo de los procesos se exponen y explican mediante diagramas de flujo, mientras que se justifica cada cálculo y configuración. Además, todo el código correspondiente se ha programado en lenguaje C y se expone en los anexos. También se ha revisado la normativa aplicable y se ha analizado tanto el impacto ambiental como el coste económico del producto. Por último, se proponen mejoras para futuros trabajos.The aim of this project is the firmware design for a portable medical device for EMG and LBIA measurements which will be used for the assessment of total knee arthroplasty patients to study the progression of different knee prostheses (Medial-Pivot and Ultra-Congruent). For its realization, the state of the art of the EMG and LBIA studies and applications are exposed, along with the currently used medical devices. In addition, the different digital filtering and processing techniques for these studies have been studied and implemented. Furthermore, a preliminary statistical study has been performed with LBIA data from 12 patients with total knee arthroplasty. The firmware design of this thesis includes: the acquiring data processes with the use of different ADCs (from the actual board and external, using the SPI interface) and a DAC, the corresponding signal processing and feature abstraction, the communication with an external device using an external BLE module with UART interface, the medical data encrypting process, the error handling functionality, and the battery level approximation. In this work, all the process workflows are exposed and explained using flowcharts, while every calculation and configuration is justified. In addition, all the corresponding code has been programmed using C language and exposed in the Annexes. Moreover, the applicable regulation has been reviewed, and both the environmental impact and economic cost of the product have been analyzed. Finally, improvements are proposed for future work.L'objectiu d'aquest projecte és el disseny del microprogramari d'un dispositiu mèdic portàtil per a mesures d'EMG i LBIA. L’aparell mèdic s'utilitzarà per a l'avaluació de pacients d'artroplàstia total de genoll per estudiar la progressió de dues pròtesis de genoll (Medial-Pivot i Ultra- Congruent). En el treball, s'exposa el coneixement actual dels estudis i aplicacions d'EMG i LBIA, juntament amb els dispositius comercials utilitzats actualment. A més, s'han estudiat i implementat les diferents tècniques de filtrat i processament digital dels senyals de EMG i LBIA. Addicionalment, s'ha fet un estudi estadístic preliminar amb dades de LBIA de 12 pacients amb artroplàstia total de genoll. El disseny del microprogramari d'aquesta tesi inclou: els processos d'adquisició de dades fent ús de diferents ADCs (de la pròpia placa i externs, utilitzant la interfície SPI) i un DAC, el processament dels senyals i l'abstracció de les seves característiques, la comunicació amb un dispositiu extern utilitzant un mòdul BLE extern amb interfície UART, el procés d'encriptació de les dades mèdiques, la funcionalitat de l’avaluació d'errors i l'aproximació del nivell de bateria. En aquest treball, totes les funcionalitats del dispositiu s'exposen i s'expliquen mitjançant diagrames de flux i es justifiquen els càlculs i configuracions corresponents. Tot el codi desenvolupat s'ha programat en llenguatge C i s'exposa als annexos. A més, s'ha revisat la normativa aplicable i s'ha analitzat tant l'impacte ambiental com el cost econòmic de l’aparell. Finalment, es proposen millores per a futurs desenvolupaments

Trade-off and Design optimization of the Notch filter for ultralow power ECG application

ECG acquisition, several leads combined with signals from different body parts (i.e., from the right wrist and the left ankle) are utilized to trace the electric activity of the heart. ECG acquisition board translates the body signal to six leads and processes the signal using a low-pass filter (LPF) and SAR ADC. The acquisition board is composed of: an instrumentation amplifier, a high-pass filter, a 60-Hz notch filter, and a common-level adjuster. But miniaturization or need of portable devices for measuring Bio-Potential parameters has led to design of IC’s for biomedical application with ultra-low power Because of miniaturization i.e. use of lower technology nodes has led to non-idealities which reduces the attenuation of Common Mode to differential component i.e. not CMRR. Because of this demerit the power line interference signal can’t be assumed as a common mode signal. Hence we need to design a power line interference filter to avoid the contamination of the signal

CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications

Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non- Linear (ELIN) systems. They can handle large-signals in a low power environment under half the capacitor area required by the more popular ELIN Log-domain filters. Their inherent class-AB nature stems from the odd property of the sinh function at the heart of their companding operation. Despite this early realisation, the Sinh filtering paradigm has not attracted the interest it deserves to date probably due to its mathematical and circuit-level complexity. This Thesis presents an overview of the CMOS weak inversion Sinh filtering paradigm and explains how biomedical systems of low- to audio-frequency range could benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of high order Sinh continuous–time filters and more importantly to confirm their micro-power consumption and 100+ dB of DR through measured results presented for the first time. Novel high order Sinh topologies are designed by means of a systematic mathematical framework introduced. They employ a recently proposed CMOS Sinh integrator comprising only p-type devices in its translinear loops. The performance of the high order topologies is evaluated both solely and in comparison with their Log domain counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a corresponding and also novel Log domain class-AB topology, confirming that Sinh filters constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense of higher complexity and power consumption. The theoretical findings are validated by means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a 0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of ~60dB and 74μW power consumption from 2V power supply

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

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