Design techniques for smart and energy-efficient wireless body sensor networks

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/10/2012Las redes inalámbricas de sensores corporales (en inglés: "wireless body sensor networks" o WBSNs) para monitorización, diagnóstico y detección de emergencias, están ganando popularidad y están llamadas a cambiar profundamente la asistencia sanitaria en los próximos años. El uso de estas redes permite una supervisión continua, contribuyendo a la prevención y el diagnóstico precoz de enfermedades, al tiempo que mejora la autonomía del paciente con respecto a otros sistemas de monitorización actuales. Valiéndose de esta tecnología, esta tesis propone el desarrollo de un sistema de monitorización de electrocardiograma (ECG), que no sólo muestre continuamente el ECG del paciente, sino que además lo analice en tiempo real y sea capaz de dar información sobre el estado del corazón a través de un dispositivo móvil. Esta información también puede ser enviada al personal médico en tiempo real. Si ocurre un evento peligroso, el sistema lo detectará automáticamente e informará de inmediato al paciente y al personal médico, posibilitando una rápida reacción en caso de emergencia. Para conseguir la implementación de dicho sistema, se desarrollan y optimizan distintos algoritmos de procesamiento de ECG en tiempo real, que incluyen filtrado, detección de puntos característicos y clasificación de arritmias. Esta tesis también aborda la mejora de la eficiencia energética de la red de sensores, cumpliendo con los requisitos de fidelidad y rendimiento de la aplicación. Para ello se proponen técnicas de diseño para reducir el consumo de energía, que permitan buscar un compromiso óptimo entre el tamaño de la batería y su tiempo de vida. Si el consumo de energía puede reducirse lo suficiente, sería posible desarrollar una red que funcione permanentemente. Por lo tanto, el muestreo, procesamiento, almacenamiento y transmisión inalámbrica tienen que hacerse de manera que se suministren todos los datos relevantes, pero con el menor consumo posible de energía, minimizando así el tamaño de la batería (que condiciona el tamaño total del nodo) y la frecuencia de recarga de la batería (otro factor clave para su usabilidad). Por lo tanto, para lograr una mejora en la eficiencia energética del sistema de monitorización y análisis de ECG propuesto en esta tesis, se estudian varias soluciones a nivel de control de acceso al medio y sistema operativo.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, it highlights first the unsustainable energy cost incurred by the straightforward wireless streaming of raw data to external analysis servers. Then, it introduces the need for new cross-layered design methods (beyond hardware and software boundaries) to enhance the autonomy of WBSNs for ambulatory monitoring. In fact, by embedding more onboard intelligence and exploiting electrocardiogram (ECG) specific knowledge, it is possible to perform real-time compressive sensing, filtering, delineation and classification of heartbeats, while dramatically extending the battery lifetime of cardiac monitoring systems. The paper concludes by showing the results of this new approach to design ultra-low power wearable WBSNs in a real-life platform commercialized by SmartCardia. This wearable system allows a wide range of applications, including multi-lead ECG arrhythmia detection and autonomous sleep monitoring for critical scenarios, such as monitoring of the sleep state of airline pilot

A real-time data mining technique applied for critical ECG rhythm on handheld device

Sudden cardiac arrest is often caused by ventricular arrhythmias and these episodes can lead to death for patients with chronic heart disease. Hence, detection of such arrhythmia is crucial in mobile ECG monitoring. In this research, a systematic study is carried out to investigate the possible limitations that are preventing the realisation of a real-time ECG arrhythmia data-mining algorithm suitable for application on mobile devices. Based on the findings, a computationally lightweight algorithm is devised and tested. Ventricular tachycardia (VT) is the most common type of ventricular arrhythmias and is also the deadliest.. A ventricular tachycardia (VT) episode is due to a disorder ofthe regular contractions ofthe heart. It occurs when the human heart ventricles generate a rapid heartbeat which disrupts the regular physiology cycle. The normal sinus rhythm (NSR) of a regular human heart beat signal has its signature PQRST waveform and in regular pattern. Whereas, the characteristics of a ventricular tachycardia (VT) signal waveforms are short R-R intervals, widen QRS duration and the absence of P-waves. Each type of ECG arrhythmia previously mentioned has a unique waveform signature that can be exploited as features to be used for the realization of an automated ECG analysis application. In order to extract this known ECG waveform feature, a time-domain analysis is proposed for feature extraction. Cross-correlation allows the computation of a co-efficient that quantifies the similarity between two times-series. Hence, by cross-correlating known ECG waveform templates with an unknown ECG signal, the coefficient can indicate the similarities. In previous published work, a preliminary study was carried out. The cross-correlation coefficient wave (CCW) technique was introduced for feature extraction. The outcome ofthis work presents CCW as a promising feature to differentiate between NSR, VT and Vfib signals. Moreover, cross-correlation computation does not require high computational overhead. Next, an automated detection algorithm requires a classification mechanism to make sense of the feature extracted. A further study is conducted and published, a fuzzy set k-NN classifier was introduced for the classification of CCW feature extracted from ECG signal segments. A training set of size 180 is used. The outcome of the study indicates that the computationally light-weight fuzzy k-NN classifier can reliably classify between NSR and VT signals, the class detection rate is low for classifying Vfib signal using the fuzzy k-NN classifier. Hence, a modified algorithm known as fuzzy hybrid classifier is proposed. By implementing an expert knowledge based fuzzy inference system for classification of ECG signal; the Vfib signal detection rate was improved. The comparison outcome was that the hybrid fuzzy classifier is able to achieve 91.1% correct rate, 100% sensitivity and 100% specificity. The previously mentioned result outperforms the compared classifiers. The proposed detection and classification algorithm is able to achieve high accuracy in analysing ECG signal feature of NSR, VT and Vfib nature. Moreover, the proposed classifier is successfully implemented on a smart mobile device and it is able to perform data-mining of the ECG signal with satisfiable results

A modular low-complexity ECG delineation algorithm for real-time embedded systems

This work presents a new modular and lowcomplexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform realtime delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in run time to a wide range of modes and sampling rates, from a Ultra-low power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete High-accuracy delineation mode in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography (CSE) committee in the High-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultra-low-power 8-MHz TI MSP430 series microcontroller ranges from 0.2 to 8.5% according to the mode used

PROCESS AWARE ANALOG-CENTRIC SINGLE LEAD ECG ACQUISITION AND CLASSIFICATION CMOS FRONTEND

The primary objective of this research work is the development of a low power single-lead ECG analog front-end (AFE) architecture which includes acquisition, digitization, process aware efficient gain and frequency control mechanism and a low complexity classifier for the detecting asystole, extreme bardycardia and tachycardia. Recent research on ECG recording systems focuses on the design of a compact single-lead wearable/portable devices with ultra-low-power consumption and in-built hardware for diagnosis and prognosis. Since, the amplitude of the ECG signal varies from hundreds of µV to a few mV, and has a bandwidth of DC to 250 Hz, conventional front-ends use an instrument amplifier followed by a programmable gain amplifier (PGA) to amplify the input ECG signal appropriately. This work presents an mixed signal ECG fronted with an ultra-low power two-stage capacitive-coupled signal conditioning circuit (or an AFE), providing programmable amplification along with tunable 2nd order high pass and lowpass filter characteristics. In the contemporary state-of-the-art ECG recording systems, the gain of the amplifier is controlled by external digital control pins which are in turn dynamically controlled through a DSP. Therefore, an efficient automatic gain control mechanism with minimal area overhead and consuming power in the order of nano watts only. The AGC turns the subsequent ADC on only after output of the PGA (or input of the ADC) reaches a level for which the ADC achieves maximum signal-to-noise-ratio (SNR), hence saving considerable startup power and avoiding the use of DSP. Further, in any practical filter design, the low pass cut-off frequency is prone to deviate from its nominal value across process and temperature variations. Therefore, post-fabrication calibration is essential, before the signal is fed to an ADC, to minimize this deviation, prevent signal degradation due to aliasing of higher frequencies into the bandwidth for classification of ECG signals, to switch to low resolution processing, hence saving power and enhances battery lifetime. Another short-coming noticed in the literature published so far is that the classification algorithm is implemented in digital domain, which turns out to be a power hungry approach. Moreover, Although analog domain implementations of QRS complexes detection schemes have been reported, they employ an external micro-controller to determine the threshold voltage. In this regard, finally a power-efficient low complexity CMOS fully analog classifier architecture and a heart rate estimator is added to the above scheme. It reduces the overall system power consumption by reducing the computational burden on the DSP. The complete proposed scheme consists of (i) an ultra-low power QRS complex detection circuit using an autonomous dynamic threshold voltage, hence discarding the need of any external microcontroller/DSP and calibration (ii) a power efficient analog classifier for the detection of three critical alarm types viz. asystole, extreme bradycardia and tachycardia. Additionally, a heart rate estimator that provides the number of QRS complexes within a period of one minute for cardiac rhythm (CR) and heart rate variability (HRV) analysis. The complete proposed architecture is implemented in UMC 0.18 µm CMOS technology with 1.8 V supply. The functionality of each of the individual blocks are successfully validated using postextraction process corner simulations and through real ECG test signals taken from the PhysioNet database. The capacitive feedback amplifier, Σ∆ ADC, AGC and the AFT are fabricated, and the measurement results are discussed here. The analog classification scheme is successfully validated using embed NXP LPC1768 board, discrete peak detector prototype and FPGA software interfac

An automated approach: from physiological signals classification to signal processing and analysis

By increased and widespread usage of wearable monitoring devices a huge volume of data is generated which requires various automated methods for analyzing and processing them and also extracting useful information from them. Since it is almost impossible for physicians and nurses to monitor physical activities of their patients for a long time, there is a need for automated data analysis techniques that abstract the information and highlight the significant events for clinicians and healthcare experts. The main objective of this thesis work was towards an automatic digital signal processing approach from physiological signal classification to processing and analyzing the two most vital physiological signals in long-term healthcare monitoring (ECG and IP). At the first stage, an automated generic physiological signal classifier for detecting an unknown recorded signal was introduced and then different algorithms for processing and analyzing the ECG and IP signals were developed and evaluated. This master thesis was a part of DISSE project which its aim was to design a new health-care system with the aim of providing medical expertise more accessible, affordable, and convenient. In this work, different publicly available databases such as MIT-BIH arrhythmia and CEBS were used in the development and evaluation phases. The proposed novel generic physiological signal classifier has the ability to distinguish five types of physiological signals (ECG, Resp, SCG, EMG and PPG) from each other with 100 % accuracy. Although the proposed classifier was not very successful in distinguishing lead I and II of ECG signal from each other (error of 27% was reported) which means that the general purpose features were enough discriminating to recognize different physiological signals from each other but not enough for classifying different ECG leads. For ECG processing and analysis section, three QRS detection methods were implemented which modified Pan-Tompkins gave the best performance with 97% sensitivity and 96,45% precision. The morphological based ectopic detection method resulted in sensitivity of 85,74% and specificity of 84,34%. Furthermore, for the first PVC detection algorithm (sum of trough) the optimal threshold and range were studied according to the AUC of ROC plot which the highest sensitivity and specificity were obtained with threshold of −5 and range of 11 : 25 that were equal to 87% and 82%, respectively. For the second PVC detection method (R-peak with minimum) the best performance was achieved with threshold of −0.7 that resulted in sensitivity of 68% and specificity of 72%. In the IP analysis section, an ACF approach was implemented for respiratory rate estimation. The estimated respira- tion rate obtained from IP signal and oronasal mask were compared and the total MAE and RMSE errors were computed that were equal to 0.40 cpm and 1.20 cpm, respectively. The implemented signal processing techniques and algorithms can be tested and improved with measured data from wearable devices for ambulatory applications

Modelado robusto para la extracción de información en entornos biofísicos y críticos

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 12/07/2018The era of information and Big Data is an environment where multiple devices, always connected, generate huge volumes of information (paradigm of the Internet of Things). This paradigm is present in different areas: the Smart Cities, sport tracking, lifestyle, or health. The goal of this thesis is the development and implementation of a Robust predictive modeling methodology using low cost wearable devices in biophysical and critical scenarios. In this manuscript we present a multilevel architecture that covers from the on-node data processing, up to the data management in Data Centers. The methodology applies energy aware optimization techniques at each level of the network. And the decision system makes use of data from different sources leading to expert decision system...La era de la información y el Big Data, se sustenta en un entorno en el que múltiples dispositivos, siempre conectados, generan ingentes volúmenes de información (paradigma del Internet de las Cosas). Este paradigma ha llegado diversos entornos: las denominadas ciudades inteligentes, monitorización deportiva, estilo de vida, o salud. El objetivo de esta tesis es el desarrollo e implementación de una metodología de modelado predictivo robusto mediante dispositivos wearable de bajo coste en entornos biofísicos y críticos. A lo largo de este manuscrito se presenta una arquitectura multinivel que abarca desde el tratamiento de los datos en los dispositivos sensores hasta el manejo de éstos en centros de datos. La metodología cubre la optimización energética a todos los niveles con consciencia del estado de la red. Y el sistema de decisión hace uso de datos de distintas fuentes para conformar un sistema experto de decisión...Fac. de InformáticaTRUEunpu

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Design and Application of Wireless Body Sensors

Hörmann T. Design and Application of Wireless Body Sensors. Bielefeld: Universität Bielefeld; 2019