Analog and Mixed Signal Design towards a Miniaturized Sleep Apnea Monitoring Device

Sleep apnea is a sleep-induced breathing disorder with symptoms of momentary and often repetitive cessations in breathing rhythm or sustained reductions in breathing amplitude. The phenomenon is known to occur with varying degrees of severity in literally millions of people around the world and cause a range of chronicle health issues. In spite of its high prevalence and serious consequences, nearly 80% of people with sleep apnea condition remain undiagnosed. The current standard diagnosis technique, termed polysomnography or PSG, requires the patient to schedule and undergo a complex full-night sleep study in a specially-equipped sleep lab. Due to both high cost and substantial inconvenience, millions of apnea patients are still undiagnosed and thus untreated. This research work aims at a simple, reliable, and miniaturized solution for in-home sleep apnea diagnosis purposes. The proposed solution bears high-level integration and minimal interference with sleeping patients, allowing them to monitor their apnea conditions at the comfort of their homes. Based on a MEMS sensor and an effective apnea detection algorithm, a low-cost single-channel apnea screening solution is proposed. A custom designed IC chip implements the apnea detection algorithm using time-domain signal processing techniques. The chip performs autonomous apnea detection and scoring based on the patient’s airflow signals detected by the MEMS sensor. Variable sensitivity is enabled to accommodate different breathing signal amplitudes. The IC chip was fabricated in standard 0.5-μm CMOS technology. A prototype device was designed and assembled including a MEMS sensor, the apnea detection IC chip, a PSoC platform, and wireless transceiver for data transmission. The prototype device demonstrates a valuable screening solution with great potential to reach the broader public with undiagnosed apnea conditions. In a battery-operated miniaturized medical device, an energy-efficient analog-to-digital converter is an integral part linking the analog world of biomedical signals and the digital domain with powerful signal processing capabilities. This dissertation includes the detailed design of a successive approximation register (SAR) ADC for ultra-low power applications. The ADC adopts an asynchronous 2b/step scheme that halves both conversion time and DAC/digital circuit’s switching activities to reduce static and dynamic energy consumption. A low-power sleep mode is engaged at the end of all conversion steps during each clock period. The technical contributions of this ADC design include an innovative 2b/step reference scheme based on a hybrid R-2R/C-3C DAC, an interpolation-assisted time-domain 2b comparison scheme, and a TDC with dual-edge-comparison mechanism. The prototype ADC was fabricated in 0.18μm CMOS process with an active area of 0.103 mm^(2), and achieves an ENoB of 9.2 bits and an FoM of 6.7 fJ/conversion-step at 100-kS/s

Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup

Midsagittal Jaw Movement Analysis for the Scoring of Sleep Apneas and Hypopneas

Given the importance of the detection and classification of sleep apneas and hypopneas (SAHs) in the diagnosis and the characterization of the SAH syndrome, there is a need for a reliable noninvasive technique measuring respiratory effort. This paper proposes a new method for the scoring of SAHs based on the recording of the midsagittal jaw motion (MJM, mouth opening) and on a dedicated automatic analysis of this signal. Continuous wavelet transform is used to quantize respiratory effort from the jaw motion, to detect salient mandibular movements related to SAHs and to delineate events which are likely to contain the respiratory events. The classification of the delimited events is performed using multilayer perceptrons which were trained and tested on sleep data from 34 recordings. Compared with SAHs scored manually by an expert, the sensitivity and specificity of the detection were 86.1% and 87.4%, respectively. Moreover, the overall classification agreement in the recognition of obstructive, central, and mixed respiratory events between the manual and automatic scorings was 73.1%. The MJM signal is hence a reliable marker of respiratory effort and allows an accurate detection and classification of SAHs

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

Analysis of Wireless Body-Centric Medical Sensors for Remote Healthcare

Aquesta tesi aborda el problema de trobar solucions confortables, de baixa potència i sense fils per aplicacions mèdiques. La tesi tracta els avantatges i les limitacions de tres tecnologies de comunicació diferents per la mesura de paràmetres del cos i mètodes per redissenyar sensors per avaluacions òptimes centrades en el cos. La tecnologia RFID es considera una de les solucions més influents per superar el problema del consum d'energia limitat, a causa de la presència de molts sensors connectats. També s'ha estudiat la tecnologia Bluetooth de baixa energia per resoldre els problemes de seguretat i la distància de lectura que, en general, representen el coll d'ampolla de RFID pels sensors de cos. Els dispositius analògics poden reduir dràsticament les necessitats d'energia a causa dels sensors i les comunicacions, considerant pocs elements i un mètode de transmissió simple. S'estudia un mètode de comunicació completament passiu, basat en FSS, que permet una distància de lectura raonable amb capacitats de detecció precises i confiables, que s'ha discutit en aquesta tesi. L'objectiu d'aquesta tesi és investigar múltiples tecnologies sense fils per dispositius portàtils per identificar solucions adequades per aplicacions particulars en el camp mèdic. El primer objectiu és demostrar la facilitat d'ús de les tecnologies econòmiques sense bateria com un indicador útil de paràmetres fisiopatològics mitjançant la investigació de les propietats de les etiquetes RFID. A més a més, s'ha abordat un aspecte més complex respecte a l'ús de petits components passius com sensors sense fils per trastorns del son. Per últim, un altre objectiu de la tesi és el desenvolupament d'un sistema completament autònom que utilitzi tecnologia BLE per obtenir propietats avançades mantenint baix tant el consum com el preuEsta tesis aborda el problema de encontrar soluciones confortables, inalámbricas y de baja potencia para aplicaciones médicas. La tesis discute las ventajas y limitaciones de tres tecnologías de comunicación diferentes para la medición en el cuerpo y los métodos para elegir y remodelar los sensores para evaluaciones óptimas centradas en el cuerpo. La tecnología RFID se considera una de las soluciones más influyentes para superar el consumo de energía limitado debido a la presencia de muchos sensores conectados. Además, la baja energía de Bluetooth se ha estudiado se ha estudiado la tecnologia Bluetooth de baja energia para resolver los problemas de seguridad y la distancia de lectura que, en general, representan el cuello de botella de la RFID para los sensores de cuerpo. Los dispositivos analógicos pueden reducir drásticamente las necesidades de energía debido a los sensores y las comunicaciones, considerando pocos elementos y un método de transmisión simple. Se estudia un método de comunicación completamente pasivo, basado en FSS, que permite una distancia de lectura razonable con capacidades de detección precisas y confiables, que se ha discutido en esta tesis. El objetivo de esta tesis es investigar múltiples tecnologías inalámbricas para dispositivos portátiles para identificar soluciones adecuadas para aplicaciones particulares en campos médicos. El primer objetivo es demostrar la facilidad de uso de las tecnologías económicas sin batería como un indicador útil de dichos parámetros fisiopatológicos mediante la investigación de las propiedades de las etiquetas RFID. Además, se ha abordado un aspecto más complejo con respecto al uso de pequeños componentes pasivos como sensores inalámbricos para enfermedades del sueño. Por último, un resultado de la tesis es desarrollar un sistema completamente autónomo que utilice la tecnología BLE para obtener propiedades avanzadas que mantengan la baja potencia y un precio bajo.This thesis addresses the problem of comfortable, low powered and, wireless solutions for specific body-worn sensing. The thesis discusses advantages and limitations of three different communication technologies for on body measurement and investigate methods to reshape sensors for optimum body-centric assessments. The RFID technology is considered one of the most influential solutions to overcome the limitated power consumption due to the presence of many sensors connected. Further, the Bluetooth low energy has been studied to solve security problems and reading distance that overall represent the bottleneck of the RFID for the body-worn sensors. Analog devices can drastically reduce the energy needs due to the sensors and the communications, considering few elements and a simple transmitting method. An entirely passive communication method, based on FSS is studied, enabling a reasonable reading distance with precise and reliable sensing capabilities, which has been discussed in this thesis. The objective of this thesis is to investigate multiple wireless technologies for wearable devices to identify suitable solutions for particular applications in medical fields. The first objective is to demonstrate the usability of the inexpensive battery-less technologies as a useful indicator of such a physio-pathological parameters by investigating the properties of the RFID tags. Furthermore, a more complex aspect regards the use of small passive components as wireless sensors for sleep diseases has been addressed. Lastly, an outcome of the thesis is to develop an entirely autonomous system using the BLE technology to obtain advanced properties keeping low power and a low price

Review on biomedical sensors, technologies, and algorithms for diagnosis of sleep-disordered breathing: Comprehensive survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB

Environmental benefits of sleep apnoea detection in the home environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment

Computational Sleep Science: Machine Learning for the Detection, Diagnosis, and Treatment of Sleep Problems from Wearable Device Data

University of Minnesota Ph.D. dissertation.December 2017. Major: Computer Science. Advisor: Jaideep Srivastava. 1 computer file (PDF); xiii, 122 pages.This thesis is motivated by the rapid increase in global life expectancy without the respective improvements in quality of life. I propose several novel machine learning and data mining methodologies for approaching a paramount component of quality of life, the translational science field of sleep research. Inadequate sleep negatively affects both mental and physical well-being, and exacerbates many non-communicable health problems such as diabetes, depression, cancer and obesity. Taking advantage of the ubiquitous adoption of wearable devices, I create algorithmic solutions to analyse sensor data. The goal is to improve the quality of life of wearable device users, as well as provide clinical insights and tools for sleep researchers and care-providers. Chapter 1 is the introduction. This section substantiates the timely relevance of sleep research for today's society, and its contribution towards improved global health. It covers the history of sleep science technology and identifies core computing challenges in the field. The scope of the thesis is established and an approach is articulated. Useful definitions, sleep domain terminology, and some pre-processing steps are defined. Lastly, an outline for the remainder of the thesis is included. Chapter 2 dives into my proposed methodology for widespread screening of sleep disorders. It surveys results from the application of several statistical and data mining methods. It also introduces my novel deep learning architecture optimized for the unique dimensionality and nature of wearable device data. Chapter 3 focuses on the diagnosis stage of the sleep science process. I introduce a human activity recognition algorithm called RAHAR, Robust Automated Human Activity Recognition. This algorithm is unique in a number of ways, including its objective of annotating a behavioural time series with exertion levels rather than activity type. Chapter 4 focuses on the last step of the sleep science process, therapy. I define a pipeline to identify \textit{behavioural recipes}. These \textit{recipes} are the target behaviour that a user should complete in order to have good quality sleep. This work provides the foundation for building out a dynamic real-time recommender system for wearable device users, or a clinically administered cognitive behavioural therapy program. Chapter 5 summarizes the impact of this body of work, and takes a look into next steps. This chapter concludes my thesis

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare