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

Hardware 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 propòsit d'aquest projecte és el disseny del hardware d'un dispositiu mèdic portàtil per a mesurar senyals d'electromiografia (EMG) i bioimpedància localitzada (LBIA), que s'utilitzarà per avaluar la progressió de dues pròtesis de genoll (Medial-Pivot i Ultra- Congruent) en pacients operats d'una artroplàstia total de genoll per a l'hospital Germans Trias i Pujol de Badalona. Per això, s'ha realitzat un estudi complet sobre els senyals d'EMG i LBIA, per tal de definir les característiques necessàries de l'equip mèdic i poder optimitzar el disseny electrònic. Per l'adquisició de senyals EMG, s'ha dissenyat i simulat un sistema compost per diferents fases, que treballen independentment per adquirir, amplificar, filtrar i adaptar el senyal EMG pel seu futur processament digital. D'altra banda, per obtenir valors de la bioimpedància localitzada dels diferents músculs que conformen el quàdriceps, s'ha dissenyat un sistema compost per dos grans blocs; el primer bloc és l'etapa d'injecció, on es genera i s'injecta un senyal feble de corrent altern a la zona a mesurar, mentre que el segon bloc, és l'etapa d'adquisició de senyals. Aquest últim s'encarrega d'adquirir la diferència de voltatge produïda per la injecció de corrent al múscul (anteriorment mencionat) per després calcular la bioimpedància a partir de la llei d'ohm. Tots els senyals són digitalitzats mitjançant el microcontrolador STM32F407VG, que s'encarregarà de processar i aconseguir les dades claus per determinar quina de les deus pròtesis desenvolupa una millor funció mecànica i una millor adaptació biològica. És important remarcar que tot el disseny, sigui per a EMG o LBIA s'ha dut a terme de manera discreta sense fer servir Front-Ends comercials o integrats complexos més que l'amplificador d'instrumentació o ADC. En addició, el present treball inclou una primera estimació dels costos de producció i fabricació per a una sola unitat, càlculs de consums i funcionament (sorolls, CMRR del sistema i amplada de banda) i una simulació completa d'EMG i LBIA per observar com funciona i es du a terme cada etapa del circuit. Finalment, en tractar-se d'un equip mèdic, també s'ha revisat la normativa aplicable i se n'ha analitzat l'impacte ambiental, s'ha proposat i definit diferents punts per a futurs treballs, com podria ser la validació i testatge de l'equip, càlculs més aproximats de consums i perfilar la bill of materials (BOM) per a grans demandes de components.The purpose of this project is the hardware design of a portable medical device to measure electromyography (EMG) and localized bioimpedance (LBIA) signals, which will be used to evaluate the adaptability and progression of two knee prostheses (medial-pivot and ultra-congruent) in patients undergoing total knee arthroplasty at the Germans Trias i Pujol Hospital in Badalona. For this, the present work undercovers the relevant properties of the EMG and LBIA signals in order to define the characteristics of the medical equipment and thus optimize its electronic design. For the EMG measurements, a system made up of different stages has been designed and simulated. These phases work independently to acquire, amplify, filter, and adapt the EMG signal for its further digital processing. On the other hand, to obtain the bioimpedance values of different quadriceps muscles, a system composed of two large blocks has been designed; the first is the injection block, where a weak alternating current signal is generated and injected into the area to be measured, while the second block is the signal acquisition stage. The purpose of the latter is to acquire the voltage difference produced by the injection of current (mentioned above) and then obtain the bioimpedance from Ohm's law. All the signals are digitized from the STM32F407VG microcontroller, which will be in charge of processing and obtaining the key data to determine which of the two prostheses performs a better mechanical function and biological adaptation. It is important to note that the entire design, whether for EMG or LBIA, has been developed discreetly without using commercial Front-Ends or complex ICs other than the instrumentation amplifier or ADC. In addition, the thesis includes a first estimation of the production and manufacturing costs for a single unit, calculations of consumption and work operation (noise, CMRR of the system and bandwidth) and a complete simulation of EMG and LBIA to observe how it works on each stage for both circuits. Finally, as it is a medical device, the applicable regulations have also been reviewed and its environmental impact has been analysed. Additionally, different points have been proposed and defined for future work, such as the construction of the PCB and its respective validation, improving both the consumption calculations and the list of materials (BOM) for large component demands.El propósito de este proyecto es el diseño del Hardware de un dispositivo médico portátil para mediciones de electromiografía (EMG) y bioimpedancia localizada (LBIA), que se utilizará para estudiar la evolución de la adaptabilidad y funcionamiento de dos prótesis de rodilla (medial-pívot y ultracongruente) en pacientes operados de artroplastia total de rodilla en el Hospital Germans Trias i Pujol de Badalona. Para ello, se ha realizado un estudio exhaustivo sobre las propiedades de las señales de EMG y LBIA con la finalidad de definir las características del equipo médico y de esta forma, optimizar el diseño electrónico del mismo. Para la lectura de mediciones EMG, se ha diseñado y simulado un sistema constituido por distintas etapas, que trabajan independientemente para adquirir, amplificar, filtrar, y adaptarla señal EMG para su posterior procesado digital. Por otro lado, para obtener los valores de bioimpedancia de distintos músculos del cuádriceps, se ha diseñado un sistema compuesto por dos grandes bloques; el primero es el bloque de inyección, donde se genera y se inyecta una señal débil de corriente alterna en la zona a medir, mientras que el segundo bloque es la etapa de adquisición de señales. Esta última tiene como finalidad adquirir la diferencia de voltaje producido por la inyección de corriente (anteriormente mencionada) para después obtener la bioimpedancia a partir de la ley de ohm. Todas las señales son digitalizadas a partir del microcontrolador STM32F407VG, que se encargará de procesar y obtener los datos claves para determinar cuál de las dos prótesis desempeña una mejor función mecánica y adaptación biológica. Es importante remarcar que todo el diseño, ya sea para EMG o LBIA, se ha desarrollado de manera discreta sin usar Front-Ends comerciales o integrados complejos más que el amplificador de instrumentación o ADC. En adición, la tesis incluye una primera estimación de los costes de producción y fabricación para una sola unidad, cálculos de consumos y funcionamiento (ruidos, CMRR del sistema y ancho de banda) y una simulación completa de EMG y LBIA para observar cómo funciona y se desarrolla cada etapa de los distintos circuitos. Finalmente, al tratarse de un equipo médico, también se ha revisado la normativa aplicable y se ha analizado el impacto ambiental del mismo. Por último, se han propuesto y definido distintos puntos para futuros trabajos, como es la construcción de la PCB y su respectiva validación, realizar cálculos más aproximados de consumos y perfilar la lista de materiales (BOM) para grandes demandas de componentes

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

A wearable heart monitor at the ear using ballistocardiogram (BCG) and electrocardiogram (ECG) with a nanowatt ECG heartbeat detection circuit

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 132-137).This work presents a wearable heart monitor at the ear that uses the ballistocardiogram (BCG) and the electrocardiogram (ECG) to extract heart rate, stroke volume, and pre-ejection period (PEP) for the application of continuous heart monitoring. Being a natural anchoring point, the ear is demonstrated as a viable location for the integrated sensing of physiological signals. The source of periodic head movements is identified as a type of BCG, which is measured using an accelerometer. The head BCG's principal peaks (J-waves) are synchronized to heartbeats. Ensemble averaging is used to obtain consistent J-wave amplitudes, which are related to stroke volume. The ECG is sensed locally near the ear using a single-lead configuration. When the BCG and the ECG are used together, an electromechanical duration called the RJ interval can be obtained. Because both head BCG and ECG have low signal-to-noise ratios, cross-correlation is used to statistically extract the RJ interval. The ear-worn device is wirelessly connected to a computer for real time data recording. A clinical test involving hemodynamic maneuvers is performed on 13 subjects. The results demonstrate a linear relationship between the J-wave amplitude and stroke volume, and a linear relationship between the RJ interval and PEP. While the clinical device uses commercial components, a custom integrated circuit for ECG heartbeat detection is designed with the goal of reducing power consumption and device size. With 58nW of power consumption, the ECG circuit replaces the traditional instrumentation amplifier, analog-to-digital converter, and signal processor with a single chip solution. The circuit demonstrates a topology that takes advantage of the ECG's characteristics to extract R-wave timings at the chest and the ear in the presence of baseline drift, muscle artifact, and signal clipping.by David Da He.Ph.D

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

Sleep studies in mice - open and closed loop devices for untethered recording and stimulation

Sleep is an important biological processes that has been studied extensively to date. Research in sleep typically involves mice experiments that use heavy benchtop equipment or basic neural loggers to record ECoG/EMG signals which are then processed offline in workstations. These systems limit the complexity of experiments that can be carried out to only simple open loop recordings, due to either the tethered setup used, which restricts animal movements, or the lack of devices that can offer more advanced features without compromising its portability. With rising popularity in exploring more physiological features that can affect sleep, such as temperature, whose importance has been highlighted in several papers [1][2][3] and advances in optogenetic stimulation, allowing high temporal and spatial neural control, there is now an unprecedented demand for experimental setups using new closed loop paradigms. To address this, this thesis presents compact and lightweight neural logging devices that are not only capable of measuring ECoG and EMG signals for core sleep analysis but also capable of taking high resolution temperature recordings and delivering optogenetic stimulus with fully adjustable parameters. Together with its embedded on-board automatic sleep stage scoring algorithm, the device will allow researchers for the first time to be able to quickly uncover the role a neural circuit plays in sleep regulation through selective neural stimulation when the animal is under the target sleep vigilance state. Original contributions include: the development of two novel multichannel neural logging devices, one for core sleep analysis and another for closed loop experimentation; the development and implementation of a lightweight, fast and highly accurate automatic on-line sleep stage scoring algorithm; and the development of a custom optogenetic coupler that is compatible with most current optogenetic setups for LED-Optical fibre coupling.Open Acces

Desenvolvimento de metodologia baseada em aprendizado por reforço e Sistema de Inferência Fuzzy para identificação e minimização de contaminantes em sinais de sEMG com aplicação em identificação de movimentos do segmento mão-braço

A incessante busca por novas tecnologias que proporcionem aumento da qualidade de vida do ser humano tem norteado a pesquisa acadêmica ao longo da história. Isso é observado na evolução dos meios de transporte, dos dispositivos de comunicação e até mesmo de serviços como o bancário. No entanto, para pessoas com deficiência motora, em especial aquelas que sofreram amputação ou não possuem parte do membro superior, a conquista de melhores condições de vida está potencialmente relacionada com liberdade e independência. Visando suprir esta necessidade, muitos pesquisadores têm trabalhado no desenvolvimento de algoritmos preditores de movimento do segmento mão-braço a partir de sinais de eletromiografia para o controle de próteses na expectativa de aumentar o número de graus de liberdade do dispositivo. Contudo, para que se obtenha sistemas eficientes e que tenham elevados índices de assertividade, é imprescindível que o nível de interferência e ruído, os quais inevitavelmente estão presentes nos registros de eletromiografia devido à instrumentação, ambiente, aspectos fisiológicos, dentre outros, seja o menor possível. Neste contexto, alguns trabalhos foram desenvolvidos visando a minimização do efeito de interferências no classificador, contudo todos aqueles abrangidos pela pesquisa realizada demandam um estágio de treinamento off-line, não são adaptáveis às variações do sinal de EMG e/ou dependem do sinal dos outros canais de medição para a minimização do efeito degradador. Diante disso, a presente proposta de tese apresenta uma metodologia baseada em aprendizagem por reforço (Reinforcement Learning) e Sistema de Inferência Fuzzy para detecção, identificação do tipo e atenuação do efeito de contaminantes em registros de eletromiografia, com aplicação em sistemas de reconhecimento de gestos do membro superior. O mesmo está fundamentado em um modelo de agente e ambiente, sendo constituído dos seguintes elementos: ambiente (atividade elétrica muscular), estado (conjunto de 6 características extraídas do sinal de EMG), ações (aplicação de filtros/procedimentos específicos para a redução do impacto de cada interferência) e agente (controlador que fará a identificação do tipo da contaminação e executará a ação adequada). Para cada ação exercida pelo agente será atribuída uma recompensa a qual, por sua vez, é determinada em virtude do impacto da primeira nas características do sinal (estado) por meio de um Sistema de Inferência Fuzzy. O treinamento, realizado através do método Ator-Crítico, consiste na obtenção de uma política de ações que maximize a recompensa percebida a longo prazo. Por meio de um experimento realizado de forma off-line conseguiu-se taxas de acerto de 92,96% na identificação de 4 tipos de contaminantes (interferência por eletrocardiografia (ECG), artefato de movimento, interferência eletromagnética oriunda da rede de energia elétrica e ruído branco gaussiano) e 69,5% quando se considerou também sinal íntegro. Além disso, por meio de um estudo de caso simulando-se o treinamento online do agente evidenciou-se que o modelo de Transfer Learning adotado foi eficaz na dispensa da necessidade do uso de dados adquiridos previamente do usuário além de acelerar o processo de aprendizado. Estas propriedades são fundamentais para a implementação de qualquer sistema de forma online. Logo, verificou-se indícios de que o SIF-ACRL tem, de fato, potencial para ser implementado de forma online.The incessant search for new technologies that provide increased quality of life for human beings has guided academic research throughout history. This is observed in the evolution of transports, communication devices and even services such as banking. However, for people with motor disabilities, especially those who have had an amputation or do not have part of the upper limb, achieving better living conditions is potentially related to freedom and independence. To meet this need, many researchers have been working on the development of hand-arm segment movement predictors algorithms from electromyography signals for the control of prostheses in the hope of increasing the device's degrees of freedom. However, to obtain efficient systems that have high levels of assertiveness, it is essential that the interference and noise level, which are inevitably present in the electromyography records due to the instrumentation, environment, physiological aspects, among others, is the lowest possible. In this context, some works were developed aiming at minimizing the effect of interference in the classifier, however, all those covered by the performed research demand an offline training stage, are not adaptable to the EMG signal variations, and/or depend on the signal of others measurement channels to minimize the degrading effect. In view of this, the present thesis proposal presents a methodology based on Reinforcement Learning and Fuzzy Inference System for detection, identification of the type and mitigation of the effect of contaminants in electromyography records, with application in gesture recognition systems of the upper limb. It is based on an agent and environment model, consisting of the following elements: environment (muscle electrical activity), state (set of 6 characteristics extracted from the EMG signal), actions (application of specific filters/procedures to reduce impact of each interference) and agent (controller who will identify the type of contamination and take the appropriate action). For each action performed by the agent, a reward will be attributed which, in turn, is determined by the impact of the actions on the signal features (state) by means of a Fuzzy Inference System. The training, carried out through the Actor-Critic method, consists of obtaining an action policy that maximizes the long term perceived reward. Through an experiment carried out offline, success rates of 92.96% were achieved in the identification of 4 types of contaminants (interference by electrocardiography (ECG), motion artifact, electromagnetic interference from the electricity network and Gaussian white noise) and 69.5% when a clean signal class was added. In addition, a case study simulating the agent's online training showed that the Transfer Learning model adopted was effective in dispensing with the need to use data previously acquired from the user, in addition to accelerating the learning process. These properties are fundamental for the implementation of any system online. Therefore, there were indications that the SIF-ACRL has the potential to be implemented online

Detection of ADC clipping, quantization noise, and amplifier saturation in surface electromyography

This paper focuses on the detection and quantification of three types of noise, analog-to-digital converter (ADC) clipping, quantization noise, and amplifier saturation, in surface electromyography (sEMG) without prior information regarding the sEMG setup. ADC clipping can be detected by searching for consecutive minimum and maximum values in a signal. Quantization noise can be expressed as a signal-to-quantization noise ratio which is estimated from the smallest observable step size in the signal. Amplifier saturation is quantified using a normality test, as amplifier saturation will reduce the normality of the signal amplitude distribution. Experimental results, using simulate