Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces

Surface Electromyographic (sEMG) Transduction of Hand Joint Angles for Human Interfacing Devices (HID)

This is an investigation of the use of surface electromyography (sEMG) as a tool to improve human interfacing devices (HID) information bandwidth through the transduction of the fingertip workspace. It combines the work of Merletti et al and Jarque-Bou et al to design an open-source framework for Fingertip Workspace based Human Interfacing Devices (HID). In this framework, the fingertip workspace is defined as the system of forearm and hand muscle force through a tensor which describes hand anthropometry. The thesis discusses the electrophysiology of muscle tissue along with the anatomy and physiology of the arm in pursuit of optimizing sensor location, muscle force measurements, and viable command gestures. Algorithms for correlating sEMG to hand joint angle are investigated using MATLAB for both static and moving gestures. Seven sEMG spots and Fingertip Joint Angles recorded by Jarque Bou et al are investigated for the application of sEMG to Human Interfacing Devices (HID). Such technology is termed Gesture Computer Interfacing (GCI) and has been shown feasible through devices such as CTRL Labs interface, and models such as those of Sartori, Merletti, and Zhao. Muscles under sEMG spots in this dataset and the actions related to them are discussed, along with what muscles and hand actions are not visible within this dataset. Viable gestures for detection algorithms are discussed based on the muscles discerned to be visible in the dataset through intensity, spectral moment, power spectra, and coherence. Detection and isolation of such viable actions is fundamental to designing an EMG driven musculoskeletal model of the hand needed to facilitate GCI. Enveloping, spectral moment, power spectrum, and coherence analysis are applied to a Sollerman Hand Function Test sEMG dataset of twenty-two subjects performing 26 activities of living to differentiate pinching and grasping tasks. Pinches and grasps were found to cause very different activation patterns in sEMG spot 3 relating to flexion of digits I - V. Spectral moment was found to be less correlated with differentiation and provided information about the degree of object manipulation performed and extent of fatigue during each task. Coherence was shown to increase between flexors and extensors with intensity of task but was found corrupted by crosstalk with increasing intensity of muscular activation. Some spectral results correlated between finger flexor and extensor power spectra showed anticipatory coherence between the muscle groups at the end of object manipulation. An sEMG amplification system capable of capturing HD-sEMG with a bandwidth of 300 and 500 Hz at a sampling frequency of 2 kHz was designed for future work. The system was designed in ordinance with current IEEE research on sensor-electrode characteristics. Furthermore, discussion of solutions to open issues in HD-sEMG is provided. This work did not implement the designed wristband but serves as a literature review and open-source design using commercially available technologies

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

Neuromorphic audio processing through real-time embedded spiking neural networks.

In this work novel speech recognition and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. First, the biological behavior of the animal’s auditory system is analyzed and studied, along with the classical mechanisms of audio signal processing for sound classification, including Deep Learning techniques. Based on these studies, novel audio processing and automatic audio signal recognition systems are proposed, using a bio-inspired auditory sensor as input. A desktop software tool called NAVIS (Neuromorphic Auditory VIsualizer) for post-processing the information obtained from spiking cochleae was implemented, allowing to analyze these data for further research. Next, using a 4-chip SpiNNaker hardware platform and Spiking Neural Networks, a system is proposed for classifying different time-independent audio signals, making use of a Neuromorphic Auditory Sensor and frequency studies obtained with NAVIS. To prove the robustness and analyze the limitations of the system, the input audios were disturbed, simulating extreme noisy environments. Deep Learning mechanisms, particularly Convolutional Neural Networks, are trained and used to differentiate between healthy persons and pathological patients by detecting murmurs from heart recordings after integrating the spike information from the signals using a neuromorphic auditory sensor. Finally, a similar approach is used to train Spiking Convolutional Neural Networks for speech recognition tasks. A novel SCNN architecture for timedependent signals classification is proposed, using a buffered layer that adapts the information from a real-time input domain to a static domain. The system was deployed on a 48-chip SpiNNaker platform. Finally, the performance and efficiency of these systems were evaluated, obtaining conclusions and proposing improvements for future works.Premio Extraordinario de Doctorado U

A Three – tier bio-implantable sensor monitoring and communications platform

One major hindrance to the advent of novel bio-implantable sensor technologies is the need for a reliable power source and data communications platform capable of continuously, remotely, and wirelessly monitoring deeply implantable biomedical devices. This research proposes the feasibility and potential of combining well established, ‘human-friendly' inductive and ultrasonic technologies to produce a proof-of-concept, generic, multi-tier power transfer and data communication platform suitable for low-power, periodically-activated implantable analogue bio-sensors. In the inductive sub-system presented, 5 W of power is transferred across a 10 mm gap between a single pair of 39 mm (primary) and 33 mm (secondary) circular printed spiral coils (PSCs). These are printed using an 8000 dpi resolution photoplotter and fabricated on PCB by wet-etching, to the maximum permissible density. Our ultrasonic sub-system, consisting of a single pair of Pz21 (transmitter) and Pz26 (receiver) piezoelectric PZT ceramic discs driven by low-frequency, radial/planar excitation (-31 mode), without acoustic matching layers, is also reported here for the first time. The discs are characterised by propagation tank test and directly driven by the inductively coupled power to deliver 29 μW to a receiver (implant) employing a low voltage start-up IC positioned 70 mm deep within a homogeneous liquid phantom. No batteries are used. The deep implant is thus intermittently powered every 800 ms to charge a capacitor which enables its microcontroller, operating with a 500 kHz clock, to transmit a single nibble (4 bits) of digitized sensed data over a period of ~18 ms from deep within the phantom, to the outside world. A power transfer efficiency of 83% using our prototype CMOS logic-gate IC driver is reported for the inductively coupled part of the system. Overall prototype system power consumption is 2.3 W with a total power transfer efficiency of 1% achieved across the tiers

Power Management ICs for Internet of Things, Energy Harvesting and Biomedical Devices

This dissertation focuses on the power management unit (PMU) and integrated circuits (ICs) for the internet of things (IoT), energy harvesting and biomedical devices. Three monolithic power harvesting methods are studied for different challenges of smart nodes of IoT networks. Firstly, we propose that an impedance tuning approach is implemented with a capacitor value modulation to eliminate the quiescent power consumption. Secondly, we develop a hill-climbing MPPT mechanism that reuses and processes the information of the hysteresis controller in the time-domain and is free of power hungry analog circuits. Furthermore, the typical power-performance tradeoff of the hysteresis controller is solved by a self-triggered one-shot mechanism. Thus, the output regulation achieves high-performance and yet low-power operations as low as 12 µW. Thirdly, we introduce a reconfigurable charge pump to provide the hybrid conversion ratios (CRs) as 1⅓× up to 8× for minimizing the charge redistribution loss. The reconfigurable feature also dynamically tunes to maximum power point tracking (MPPT) with the frequency modulation, resulting in a two-dimensional MPPT. Therefore, the voltage conversion efficiency (VCE) and the power conversion efficiency (PCE) are enhanced and flattened across a wide harvesting range as 0.45 to 3 V. In a conclusion, we successfully develop an energy harvesting method for the IoT smart nodes with lower cost, smaller size, higher conversion efficiency, and better applicability. For the biomedical devices, this dissertation presents a novel cost-effective automatic resonance tracking method with maximum power transfer (MPT) for piezoelectric transducers (PT). The proposed tracking method is based on a band-pass filter (BPF) oscillator, exploiting the PT’s intrinsic resonance point through a sensing bridge. It guarantees automatic resonance tracking and maximum electrical power converted into mechanical motion regardless of process variations and environmental interferences. Thus, the proposed BPF oscillator-based scheme was designed for an ultrasonic vessel sealing and dissecting (UVSD) system. The sealing and dissecting functions were verified experimentally in chicken tissue and glycerin. Furthermore, a combined sensing scheme circuit allows multiple surgical tissue debulking, vessel sealer and dissector (VSD) technologies to operate from the same sensing scheme board. Its advantage is that a single driver controller could be used for both systems simplifying the complexity and design cost. In a conclusion, we successfully develop an ultrasonic scalpel to replace the other electrosurgical counterparts and the conventional scalpels with lower cost and better functionality

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

A low power, reconfigurable fabric body area network for healthcare applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 105-110).Body Area Networks (BANs) are gaining prominence for their capability to revolutionize medical monitoring, diagnosis and treatment. This thesis describes a BAN that uses conductive fabrics (e-textiles) worn by the user to act as a power distribution and data communication network to sensors on the user's body. The network is controlled by a central hub in the form of a Base Station, which can either be a standalone device or can be embedded inside one of the user's portable electronic devices like a cellphone. Specifications for a Physical (PHY) layer and a Medium Access Control (MAC) layer have been developed that make use of the asymmetric energy budgets between the base station and sensor nodes in the network. The PHY layer has been designed to be suitable for the unique needs of such a BAN, namely easy reconfigurability, fault-tolerance and efficient energy and data transfer at low power levels. This is achieved by a mechanism for dividing the network into groups of sensors. The co-designed MAC layer is capable of supporting a wide variety of sensors with different data rate and network access requirements, ranging from EEG monitors to temperature sensors. Circuits have been designed at both ends of the network to transmit, receive and store power and data in appropriate frequency bands. Digital circuits have been designed to implement the MAC protocols. The base station and sensor nodes have been implemented in standard 180nm 1P6M CMOS process, and occupy an area 4.8mm2 and 3.6mm2 respectively. The base station has a minimum power consumption of 2.86mW, which includes the power transmitter, modulation and demodulation circuitry. The sensor nodes can recover up to 33.6paW power to supply to the biomedical signal acquisition circuitry with peak transfer efficiency of 1.2%.by Nachiket Venkappayya Desai.S.M