A comprehensive model for power line interference in biopotential measurements

Power line interference is a major problem in high-resolution biopotential measurements. Because interference coupling is mostly capacitive, shielding electrode leads and a high common-mode rejection ratio (CMRR) are quite effective in reducing power-line interference but do not completely eliminate it. We propose a model that includes both interference external to the measuring system and interference coming from its internal power supply. Moreover, the model considers interference directly coupled to the measuring electrodes, because, as opposed to connecting leads, electrodes are not usually shielded. Experimental results confirm that reducing interference coupled through electrodes yields a negligible interference. The proposed model can be applied to other differential measurement systems, particulary those involving electrodes or sensors placed far apart.Peer Reviewe

A practical approach to electrode-skin impedance unbalance measurement

Unbalance between electrode-skin impedances is a major problem in biopotential recordings, leading to increased power-line interference. This paper proposes a simple, direct method to measure that unbalance at power-line frequency (50-60 Hz), thus allowing the determination of actual recording conditions for biopotential amplifiers. The method is useful in research, amplifier testing, electrode design and teaching purposes. It has been experimentally validated by using both phantom impedances and real electrode-skin impedances.Peer Reviewe

Human Body–Electrode Interfaces for Wide-Frequency Sensing and Communication: A Review

Several on-body sensing and communication applications use electrodes in contact with the human body. Body–electrode interfaces in these cases act as a transducer, converting ionic current in the body to electronic current in the sensing and communication circuits and vice versa. An ideal body–electrode interface should have the characteristics of an electrical short, i.e., the transfer of ionic currents and electronic currents across the interface should happen without any hindrance. However, practical body–electrode interfaces often have definite impedances and potentials that hinder the free flow of currents, affecting the application’s performance. Minimizing the impact of body–electrode interfaces on the application’s performance requires one to understand the physics of such interfaces, how it distorts the signals passing through it, and how the interface-induced signal degradations affect the applications. Our work deals with reviewing these elements in the context of biopotential sensing and human body communication

Sistema multicanal para a aquisição de biopotenciais

Se presenta un sistema compuesto por hardware de adquisición y software de soporte para la medición de biopotenciales en tiempo real desde una PC. El equipo cuenta con 8 canales diferenciales acoplados en continua, muestreados con convertidores analógico-digitales sigma-delta de 24 bits, con ganancia y tasa de muestreo configurables. La resolución del dispositivo está dada por el piso de ruido del sistema que es inferior a 2 µVrms en un ancho de banda de (0.05-100) Hz. Para medidas en un ancho de banda de 1 kHz el piso de ruido resulta menor a 3 µVrms. El coeficiente de rechazo de modo común es de 96 dB, y para lograr medidas de mayor calidad se utilizan electrodos activos y un circuito independiente para reducir la tensión de modo común, lo que posibilita utilizar topologías de medición no diferenciales. La transmisión de datos y de energía para todo el sistema se realiza a través del bus USB. El equipo cuenta con una barrera de aislamiento compatible con normas internacionales de seguridad eléctrica para equipamiento médico. Se relevó la respuesta en frecuencia y se comprobó que cumple con requisitos para dispositivos de electrocardiografía. Se adquirieron distintas señales de biopotenciales para verificar el funcionamiento del equipo y demostrar el uso del software.A biopotential measurement system composed of acquisition hardware and software capable of relaying real-time signals to a PC is presented. The device has 8 DC coupled differential channels sampled by 24 bits sigma-delta analog to digital converters with programmable gain and sampling frequency. The noise floor of the device determines its resolution, and it is less than 2 µVrms in a bandwidth of (0.05-100) Hz. Measurements up to 1 kHz can be carried out with a noise voltage less than 3 µVrms. The common mode rejection ratio is 96 dB. To achieve high-quality measurements active electrodes are used along with a common mode voltage reduction circuit allowing single-ended measurement topologies. A USB connection serves both as data channel and power source. The device includes an isolation barrier in agreement with international standards for the electrical safety of medical equipment. Its frequency response was measured and compared with accepted standards for electrocardiographic devices, and various biopotential measurements were carried out in order to test both hardware and software.é apresentado um sistema composto de hardware de aquisição e software de suporte para a medição de biopotenciais em tempo real a partir de um PC. O aparelho tem 8 canais diferenciais acopladas em contínuo, amostrados com conversores analógico-digital sigma-delta 24 bits. com o ganho e taxa de amostragem configurável. A resolução do dispositivo é determinado pelo nível de ruído do sistema é menos 2 μVrms uma largura de banda (0,05-100) Hz. Para medições em uma largura de banda de 1 kHz o piso de ruído é inferior a 3 μVrms. A razão de rejeição de modo comum é 96 dB, e para se obter medidas de qualidade superior é usado eléctrodos activos e um circuito separado para reduzir a tensão de modo comum, tornando-se possível a utilização de topologias de medição não diferenciais. A transmissão de dados e energia para todo o sistema é feito através do barramento USB. A equipe tem uma barreira de isolamento compatível com as normas de segurança elétrica internacionais para equipamentos médicos. Foi revelada a resposta em frequência e está em conformidade com os requisitos para dispositivos de eletrocardiografia. Se Obteveram diferentes sinais de biopotenciais para vereficar o funcionamento equipamento e demonstrar a utilização do software.Fil: Guerrero, Federico Nicolás. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Haberman, Marcelo Alejandro. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Spinelli, Enrique Mario. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Low-Power Wireless Medical Systems and Circuits for Invasive and Non-Invasive Applications

Approximately 75% of the health care yearly budget of public health systems around the world is spent on the treatment of patients with chronic diseases. This, along with advances on the medical and technological fields has given rise to the use of preventive medicine, resulting on a high demand of wireless medical systems (WMS) for patient monitoring and drug safety research. In this dissertation, the main design challenges and solutions for designing a WMS are addressed from system-level, using off-the-shell components, to circuit implementation. Two low-power oriented WMS aiming to monitor blood pressure of small laboratory animals (implantable) and cardiac-activity (12-lead electrocardiogram) of patients with chronic diseases (wearable) are presented. A power consumption vs. lifetime analysis to estimate the monitoring unit lifetime for each application is included. For the invasive/non-invasive WMS, in-vitro test benches are used to verify their functionality showing successful communication up to 2.1 m/35 m with the monitoring unit consuming 0.572 mA/33 mA from a 3 V/4.5 V power supply, allowing a two-year/ 88-hour lifetime in periodic/continuous operation. This results in an improvement of more than 50% compared with the lifetime commercial products. Additionally, this dissertation proposes transistor-level implementations of an ultra-low-noise/low-power biopotential amplifier and the baseband section of a wireless receiver, consisting of a channel selection filter (CSF) and a variable gain amplifier (VGA). The proposed biopotential amplifier is intended for electrocardiogram (ECG)/ electroencephalogram (EEG)/ electromyogram (EMG) monitoring applications and its architecture was designed focused on improving its noise/power efficiency. It was implemented using the ON-SEMI 0.5 µm standard process with an effective area of 360 µm2. Experimental results show a pass-band gain of 40.2 dB (240 mHz - 170 Hz), input referred noise of 0.47 Vrms, minimum CMRR of 84.3 dBm, NEF of 1.88 and a power dissipation of 3.5 µW. The CSF was implemented using an active-RC 4th order inverse-chebyshev topology. The VGA provides 30 gain steps and includes a DC-cancellation loop to avoid saturation on the sub-sequent analog-to-digital converter block. Measurement results show a power consumption of 18.75 mW, IIP3 of 27.1 dBm, channel rejection better than 50 dB, gain variation of 0-60dB, cut-off frequency tuning of 1.1-2.29 MHz and noise figure of 33.25 dB. The circuit was implemented in the standard IBM 0.18 µm CMOS process with a total area of 1.45 x 1.4 mm^(2). The presented WMS can integrate the proposed biopotential amplifier and baseband section with small modifications depending on the target signal while using the low-power-oriented algorithm to obtain further power optimization

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

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

Polypyrrole (PPy) Coated Patterned Vertical Carbon Nanotube (pvCNT) Dry ECG Electrode Integrated with a Novel Wireless Resistive Analog Passive (WRAP) ECG Sensor

Polypyrrole (PPy) Coated Patterned Vertical Carbon Nanotube (pvCNT) Dry ECG Electrode Integrated with a Novel Wireless Resistive Analog Passive (WRAP) ECG Senso

Review on Power Line Interference Removal from ECG Signal Using Adaptive and Error Filter

An ECG signal is basically an index of the functionality of the heart. For example, a physician can detect arrhythmia by studying abnormalities in the ECG signal. Since very fine features present in an ECG signal may convey important information, it is important to have the signal as clean as possible. Power line interference may be significant in electrocardiography. Often, a proper recording environment is not sufficient to avoid this interference. ECG signals polluted by power line noise of relatively large amplitude were the frequency of power line interference accurately at 50 Hz or 60 Hz, a sharp notch filter would be able to separate and eliminate the noise. The major difficulty is that the frequency can vary about fractions of a Hertz, or even a few Hertz. Two different approaches have been proposed in literature for this purpose notch filters and adaptive interference cancellers. Notch filters reduce the power line interference by suppressing predetermined frequencies. One of the possible alternatives to take frequency variations into account is the use of an external reference power line signal. An ideal EMI filter for ECG should act as a sharp notch filter to eliminate only the undesirable power line interference while automatically adapting itself to variations in the frequency and level of the noise. This technique, available by the use of adaptive filters only, is reported in literature and present serious practical difficulties and is difficult to implement