A 122 fps, 1 MHz bandwidth multi-frequency wearable EIT belt featuring novel active electrode architecture for neonatal thorax vital sign monitoring

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has sixteen active electrodes. Each has an application specific integrated circuit (ASIC) connected to an electrode. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier (IA), a digital controller and multiplexors. The wearable EIT belt features a new active electrode architecture that allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It provides intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio. The ASIC was designed in a CMOS 0.35-μm high-voltage technology. The high specification EIT belt has an image frame rate of 122 fps, a wide operating bandwidth of 1 MHz and multi-frequency operation. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1o variation across all possible channels. The image results confirmed the advantage of the new active electrode architecture and the benefit of wideband, multi-frequency EIT operation. The wearable EIT belt can also detect patient position and torso shape information using a MEMS sensor interfaced to each ASIC. The system successfully captured high quality lung respiration EIT images, breathing cycle and heart rate

A 122 fps, 1 MHz bandwidth multi-frequency wearable EIT belt featuring novel active electrode architecture for neonatal thorax vital sign monitoring

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has sixteen active electrodes. Each has an application specific integrated circuit (ASIC) connected to an electrode. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier (IA), a digital controller and multiplexors. The wearable EIT belt features a new active electrode architecture that allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It provides intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio. The ASIC was designed in a CMOS 0.35-μm high-voltage technology. The high specification EIT belt has an image frame rate of 122 fps, a wide operating bandwidth of 1 MHz and multi-frequency operation. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1o variation across all possible channels. The image results confirmed the advantage of the new active electrode architecture and the benefit of wideband, multi-frequency EIT operation. The wearable EIT belt can also detect patient position and torso shape information using a MEMS sensor interfaced to each ASIC. The system successfully captured high quality lung respiration EIT images, breathing cycle and heart rate

Advances in Integrated Circuits and Systems for Wearable Biomedical Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an impedance mapping technique that can be used to image the inner impedance distribution of the subject under test. It is non-invasive, inexpensive and radiation-free, while at the same time it can facilitate long-term and real-time dynamic monitoring. Thus, EIT lends itself particularly well to the development of a bio-signal monitoring/imaging system in the form of wearable technology. This work focuses on EIT system hardware advancement using complementary metal oxide semiconductor (CMOS) technology. It presents the design and testing of application specific integrated circuit (ASIC) and their successful use in two bio-medical applications, namely, neonatal lung function monitoring and human-machine interface (HMI) for prosthetic hand control. Each year fifteen million babies are born prematurely, and up to 30% suffer from lung disease. Although respiratory support, especially mechanical ventilation, can improve their survival, it also can cause injury to their vulnerable lungs resulting in severe and chronic pulmonary morbidity lasting into adulthood, thus an integrated wearable EIT system for neonatal lung function monitoring is urgently needed. In this work, two wearable belt systems are presented. The first belt features a miniaturized active electrode module built around an analog front-end ASIC which is fabricated with 0.35-µm high-voltage process technology with ±9 V power supplies and occupies a total die area of 3.9 mm². The ASIC offers a high power active current driver capable of up to 6 mAp-p output, and wideband active buffer for EIT recording as well as contact impedance monitoring. The belt has a bandwidth of 500 kHz, and an image frame rate of 107 frame/s. To further improve the system, the active electrode module is integrated into one ASIC. It contains a fully differential current driver, a current feedback instrumentation amplifier (IA), a digital controller and multiplexors with a total die area of 9.6 mm². Compared to the conventional active electrode architecture employed in the first EIT belt, the second belt features a new architecture. It allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It has intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio (CMRR) up to 74 dB, and with active gain, the noise level can be reduced by a factor of √3 using the adjacent scan. The second belt has a wider operating bandwidth of 1 MHz and multi-frequency operation. The image frame rate is 122 frame/s, the fastest wearable EIT reported to date. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all channels. In addition the ASIC facilitates several other functionalities to provide supplementary clinical information at the bedside. With the advancement of technology and the ever-increasing fusion of computer and machine into daily life, a seamless HMI system that can recognize hand gestures and motions and allow the control of robotic machines or prostheses to perform dexterous tasks, is a target of research. Originally developed as an imaging technique, EIT can be used with a machine learning technique to track bones and muscles movement towards understanding the human user’s intentions and ultimately controlling prosthetic hand applications. For this application, an analog front-end ASIC is designed using 0.35-µm standard process technology with ±1.65 V power supplies. It comprises a current driver capable of differential drive and a low noise (9μVrms) IA with a CMRR of 80 dB. The function modules occupy an area of 0.07 mm². Using the ASIC, a complete HMI system based on the EIT principle for hand prosthesis control has been presented, and the user’s forearm inner bio-impedance redistribution is assessed. Using artificial neural networks, bio-impedance redistribution can be learned so as to recognise the user’s intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation. Experiments with five subjects show that the system can achieve an overall recognition accuracy of 95.8%

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Wearable Sensors for Frequency-Multiplexed EIT and Multilead ECG Data Acquisition

This paper presents a wearable sensor architecture for frequency-multiplexed electrical impedance tomography (EIT) and synchronous multilead electrocardiogram (ECG) data acquisition. The system is based on a novel electronic sensing architecture, called cooperative sensors, that significantly reduces the cabling complexity and enables flexible EIT stimulation and measurement patterns. The cooperative-sensor architecture was initially designed for ECG and has been extended for multichannel bioimpedance measurement. This approach allows for an adjustable EIT stimulation pattern via frequency-division multiplexing. This paper also shows a calibration procedure as well as EIT system noise performance assessment. Preliminary measurements on a healthy volunteer showed the ability of the wearable system to measure EIT data synchronously with multilead ECG. Ventilation-related and heartbeat-related EIT images were reconstructed, demonstrating the feasibility of the proposed architecture for non-invasive cardiovascular monitoring

Towards an Efficient Gas Exchange Monitoring with Electrical Impedance Tomography - Optimization and validation of methods to investigate and understand pulmonary blood flow with indicator dilution

In vielen Fällen sind bei Patienten, die unter stark gestörtem Gasaustausch der Lunge leiden, die regionale Lungenventilation und die Perfusion nicht aufeinander abgestimmt. Besonders bei Patienten mit akutem Lungenversagen sind sehr heterogene räumliche Verteilungen von Belüftung und Perfusion der Lunge zu beobachten. Diese Patienten müssen auf der Intensivstation künstlich beatmet und überwacht werden, um einen ausreichenden Gasaustausch sicherzustellen. Bei schweren Lungenverletzungen ist es schwierig, durch die Anwendung hoher Beatmungsdrücke und -volumina eine optimale Balance zwischen dem Rekrutieren kollabierter Regionen zu finden, und gleichzeitig die Lunge vor weiterem Schaden durch die von außen angelegten Drücke zu schützen. Das Interesse für eine bettseitige Messung und Darstellung der regionalen Belüftungs- und Perfusionsverteilung für den Einsatz auf der Intensivstation ist in den letzten Jahren stark gestiegen, um eine lungenprotektive Beatmung zu ermöglichen und klinische Diagnosen zu vereinfachen. Die Elektrische-Impedanztomographie (EIT) ist ein nicht-invasives, strahlungsfreies und sehr mobil einsetzbares System. Es bietet eine hohe zeitliche Abtastung und eine funktionelle räumliche Auflösung, die es ermöglicht, dynamische (patho-) physiologische Prozesse zu visualisieren und zu überwachen. Die medizinische Forschung an EIT hat sich dabei hauptsächlich auf die Schätzung der räumlichen Belüftung konzentriert. Kommerziell erhältliche Systeme haben gezeigt, dass die EIT eine wertvolle Entscheidungshilfe während der mechanischen Beatmung darstellt. Allerdings ist die Abschätzung der pulmonalen Perfusion mit EIT noch nicht etabliert. Dies könnte das fehlende Glied sein, um die Analyse des pulmonalen Gasaustauschs am Krankenbett zu ermöglichen. Obwohl einige Publikationen die prinzipielle Machbarkeit der indikatorgestützten EIT zur Schätzung der räumlichen Verteilung des pulmonalen Blutflusses gezeigt haben, müssen diese Methoden optimiert und durch Vergleich mit dem Goldstandard des Lungenperfusions-Monitorings validiert werden. Darüber hinaus ist weitere Forschung notwendig, um zu verstehen welche physiologischen Informationen der EIT-Perfusionsschätzung zugrunde liegen. Mit der vorliegenden Arbeit soll die Frage beantwortet werden, ob bei der klinischen Anwendung von EIT neben der regionalen Belüftung auch räumliche Informationen des pulmonalen Blutflusses geschätzt werden können, um damit potenziell den pulmonalen Gasaustausch am Krankenbett beurteilen zu können. Die räumliche Verteilung der Perfusion wurde durch Bolusinjektion einer leitfähigen Kochsalzlösung als Indikator geschätzt, um die Verteilung des Indikators während seines Durchgangs durch das Gefäßsystem der Lunge zu verfolgen. Verschiedene dynamische EIT-Rekonstruktionsmethoden und Perfusionsparameter Schätzmethoden wurden entwickelt und verglichen, um den pulmonalen Blutfluss robust beurteilen zu können. Die geschätzten regionalen EIT-Perfusionsverteilungen wurden gegen Goldstandard Messverfahren der Lungenperfusion validiert. Eine erste Validierung wurde anhand von Daten einer tierexperimentellen Studie durchgeführt, bei der die Multidetektor-Computertomographie als vergleichende Lungenperfusionsmessung verwendet wurde. Darüber hinaus wurde im Rahmen dieser Arbeit eine umfassende präklinische Tierstudie durchgeführt, um die Lungenperfusion mit indikatorverstärkter EIT und Positronen-Emissions-Tomographie während mehrerer verschiedener experimenteller Zustände zu untersuchen. Neben einem gründlichen Methodenvergleich sollte die klinische Anwendbarkeit der indikatorgestützten EIT-Perfusionsmessung untersucht werden, indem wir vor allem die minimale Indikatorkonzentration analysierten, die eine robuste Perfusionsschätzung erlaubte und den geringsten Einfluss für den Patienten darstellt. Neben den experimentellen Validierungsstudien wurden zwei in-silico-Untersuchungen durchgeführt, um erstens die Sensitivität von EIT gegenüber des Durchgangs eines leitfähigen Indikators durch die Lunge vor stark heterogenem pulmonalen Hintergrund zu bewerten. Zweitens untersuchten wir die physiologischen Einflüsse, die zu den rekonstruierten EITPerfusionsbildern beitragen, um die Limitationen der Methode besser zu verstehen. Die Analysen zeigten, dass die Schätzung der Lungenperfusion auf der Basis der indikatorverstärkten EIT ein großes Potenzial für die Anwendung in der klinischen Praxis aufweist, da wir sie mit zwei Goldstandard-Perfusionsmesstechniken validieren konnten. Zudem konnten wertvolle Schlüsse über die physiologischen Einflüsse auf die geschätzten EIT Perfusionsverteilungen gezogen werden

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Wearable Sensors for Frequency-Multiplexed EIT and Multilead ECG Data Acquisition

This paper presents a wearable sensor architecture for frequency-multiplexed electrical impedance tomography (EIT) and synchronous multilead electrocardiogram (ECG) data acquisition. The system is based on a novel electronic sensing architecture, called cooperative sensors, that significantly reduces the cabling complexity and enables flexible EIT stimulation and measurement patterns. The cooperative-sensor architecture was initially designed for ECG and has been extended for multichannel bioimpedance measurement. This approach allows for an adjustable EIT stimulation pattern via frequency-division multiplexing. This work also shows a calibration procedure as well as EIT system noise performance assessment. Preliminary measurements on a hea

Diseño y Desarrollo Software para la Gestión de Sensores Biomédicos basado en la Norma X73

Cada vez se desarrollan más dispositivos capaces de obtener medidas relacionadas con la salud que tienen la necesidad de transmitir la información a sistemas con mayor capacidad de computación para el análisis de datos con el fin de permitir el diagnóstico de enfermedades, la prevención de situaciones de riesgo o la propia gestión de la enfermedad. El problema que se presenta en este ámbito es la falta de normalización en la comunicación de la información obtenida por los sensores. El estudio de la situación actual de la Norma IEEE 11073 (en adelante referida también como X73) es determinante a la hora de acercarse a una solución factible que resuelva el problema de la interoperabilidad entre dispositivos. Igualmente, conocer el estado del arte tanto del estándar Bluetooth de Baja Energía (BLE, del inglés Bluetooth Low Energy) como de su compatibilidad con el modelo de datos de X73, constituye el primer acercamiento al asunto en cuestión y sienta las bases de este trabajo. La presente propuesta pretende determinar las directrices que permitan la implementación de un software para la gestión de sensores biomédicos y garantizar la comunicación, de acuerdo con la Norma IEEE 11073, de manera exitosa entre el sensor y el dispositivo que procesará los datos. Concretamente, el diseño propuesto transfiere la información estructurada según la Norma X73 sobre un canal BLE, para un monitor de bioimpedancia diseñado por investigadores del Grupo de Ingeniería Biomédica de la Universidad de Sevilla, en el que se ha desarrollado el presente Trabajo Fin de Grado. Con la estandarización de este proceso se persigue dotar de consistencia y coherencia a este tipo de comunicaciones; de manera que cualquier dispositivo biomédico, emisor o receptor, sea capaz de enviar o recibir información conforme a un modelo común independientemente de su fabricante. El diseño de este módulo de comunicaciones conforme a la Norma IEEE 11073 abre la puerta a su aplicación en diferentes sensores y dispositivos dentro del ámbito de la e-salud, facilitando su desarrollo e integración con otros sistemas; siempre dentro del estándar establecido por la Norma IEEE 11073.We are facing more and more devices that are capable of obtaining health-related measurements needed to transmit information to systems with greater computing capacity for data analysis in order to allow the diagnosis of diseases, the prevention of risk situations or enable a person to manage their own disease. The main problem in this area is the lack of standardization in the communication of the information obtained by the sensors. The study of the current situation of the IEEE 11073 Standard (from now on referred to as X73) is decisive when approaching a feasible solution that solves the problem of interoperability between devices. Likewise, knowing the state of the art of both the Bluetooth Low Energy Standard and its compatibility with the data model of X73 constitutes the first approach to the matter in question and lays the basis for this work. The present proposal aims at determining the guidelines that allow the implementation of a software for the management of biomedical sensors and ensure a successful communication, in accordance with IEEE 11073, successfully between the sensor and the device that will process the data. Specifically, the proposed design transfers the structured information according to X73 Standard on a BLE channel, for a bioimpedance monitor designed at the Biomedical Engineering Group of the University of Seville, where this project has been developed. With the standardization of this process it is sought to provide consistency and coherence to this type of communications; so that any biomedical device, transmitter or receiver, is able to send or receive information according to a common model regardless its manufacturer. The design of this communications module according to IEEE 11073 opens the door to its application to different sensors and devices within the field of e-health, facilitating its development and integration with other systems; always within the framework established by the IEEE 11073 Standard.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació