A 0.6V 2.9µW mixed-signal front-end for ECG monitoring

This paper presents a mixed-signal ECG front-end that uses aggressive voltage scaling to maximize power-efficiency and facilitate integration with low-voltage DSPs. 50/60Hz interference is canceled using mixed-signal feedback, enabling ultra-low-voltage operation by reducing dynamic range requirements. Analog circuits are optimized for ultra-low-voltage, and a SAR ADC with a dual-DAC architecture eliminates the need for a power-hungry ADC buffer. Oversampling and ΔΣ-modulation leveraging near-V[subscript T] digital processing are used to achieve ultra-low-power operation without sacrificing noise performance and dynamic range. The fully-integrated front-end is implemented in a 0.18μm CMOS process and consumes 2.9μW from 0.6V.Texas Instruments IncorporatedNatural Sciences and Engineering Research Council of Canada (Fellowship

Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring

Many devices and solutions for remote electrocardiogram (ECG) monitoring have been proposed in the literature. These solutions typically have a large marginal cost per added sensor and are not seamlessly integrated with other smart home solutions. Here, we propose an ECG remote monitoring system that is dedicated to non-technical users in need of long-term health monitoring in residential environments and is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype consists of a complete vertical solution with a series of advantages with respect to the state of the art, considering both the prototypes with integrated front end and prototypes realized with off-the-shelf components: 1) ECG prototype sensors with record-low energy per effective number of quantized levels; 2) an architecture providing low marginal cost per added sensor/user; and 3) the possibility of seamless integration with other smart home systems through a single IoT infrastructure

Embedded computing systems design: architectural and application perspectives

Questo elaborato affronta varie problematiche legate alla progettazione e all'implementazione dei moderni sistemi embedded di computing, ponendo in rilevo, e talvolta in contrapposizione, le sfide che emergono all'avanzare della tecnologia ed i requisiti che invece emergono a livello applicativo, derivanti dalle necessità degli utenti finali e dai trend di mercato. La discussione sarà articolata tenendo conto di due punti di vista: la progettazione hardware e la loro applicazione a livello di sistema. A livello hardware saranno affrontati nel dettaglio i problemi di interconnettività on-chip. Aspetto che riguarda la parallelizzazione del calcolo, ma anche l'integrazione di funzionalità eterogenee. Sarà quindi discussa un'architettura d'interconnessione denominata Network-on-Chip (NoC). La soluzione proposta è in grado di supportare funzionalità avanzate di networking direttamente in hardware, consentendo tuttavia di raggiungere sempre un compromesso ottimale tra prestazioni in termini di traffico e requisiti di implementazioni a seconda dell'applicazione specifica. Nella discussione di questa tematica, verrà posto l'accento sul problema della configurabilità dei blocchi che compongono una NoC. Quello della configurabilità, è un problema sempre più sentito nella progettazione dei sistemi complessi, nei quali si cerca di sviluppare delle funzionalità, anche molto evolute, ma che siano semplicemente riutilizzabili. A tale scopo sarà introdotta una nuova metodologia, denominata Metacoding che consiste nell'astrarre i problemi di configurabilità attraverso linguaggi di programmazione di alto livello. Sulla base del metacoding verrà anche proposto un flusso di design automatico in grado di semplificare la progettazione e la configurazione di una NoC da parte del designer di rete. Come anticipato, la discussione si sposterà poi a livello di sistema, per affrontare la progettazione di tali sistemi dal punto di vista applicativo, focalizzando l'attenzione in particolare sulle applicazioni di monitoraggio remoto. A tal riguardo saranno studiati nel dettaglio tutti gli aspetti che riguardano la progettazione di un sistema per il monitoraggio di pazienti affetti da scompenso cardiaco cronico. Si partirà dalla definizione dei requisiti, che, come spesso accade a questo livello, derivano principalmente dai bisogni dell'utente finale, nel nostro caso medici e pazienti. Verranno discusse le problematiche di acquisizione, elaborazione e gestione delle misure. Il sistema proposto introduce vari aspetti innovativi tra i quali il concetto di protocollo operativo e l'elevata interoperabilità offerta. In ultima analisi, verranno riportati i risultati relativi alla sperimentazione del sistema implementato. Infine, il tema del monitoraggio remoto sarà concluso con lo studio delle reti di distribuzione elettrica intelligenti: le Smart Grid, cercando di fare uno studio dello stato dell'arte del settore, proponendo un'architettura di Home Area Network (HAN) e suggerendone una possibile implementazione attraverso Commercial Off the Shelf (COTS)

Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications

Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making. In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%. One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data

Internet of Things (IoT) based ECG System for Rural Health Care

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future

An ECG-on-Chip with 535-nW/Channel Integrated Lossless Data Compressor for Wireless Sensors

This paper presents a low-power ECG recording system-on-chip (SoC) with on-chip low-complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices. The chip uses a linear slope predictor for data compression, and incorporates a novel low-complexity dynamic coding-packaging scheme to frame the prediction error into fixed-length 16-bit format. The proposed technique achieves an average compression ratio of 2.25x on MIT/BIH ECG database. Implemented in a standard 0.35 um process, the compressor uses 0.565K gates/channel occupying 0.4 mm2 for four channels, and consumes 535 nW/channel at 2.4 V for ECG sampled at 512 Hz. Small size and ultra-low power consumption makes the proposed technique suitable for wearable ECG sensor applications