MR-compatible Electrophysiology Recording System for Multimodal Imaging

Simultaneous acquisition of functional magnetic resonance imaging (fMRI) and electrophysiological recordings is an emerging multimodal neuroimaging strategy for studying brain functions. However, the strong magnetic field generated during fMRI greatly degrades the electrophysiological signal quality during simultaneous acquisition. Here, I developed a low powered, miniaturized, system – “ECHO” which delivers a hardware and software solution to overcome the challenges presented by multimodal imaging. The device monitors fluctuations in electromagnetic field during fMRI and synchronizes amplification and sampling of electrophysiological signals to minimize effects of gradient and RF artifacts (electromagnetic artifacts). Furthermore, I introduced a concept of wirelessly transmitting recorded data through the MRI receiver coil. ECHO transmits the data at a frequency visible to the MRI receiver coil, after which the transmitted data is readily separable from the MRI image in the frequency domain. The MR-compatibility of the recorder was evaluated through a series of experiments with a phantom to study its effects on the MRI image quality. To further evaluate the effectiveness of ECHO, I recorded electrocardiogram and local field potential (evoked potential) in live rats during concurrent fMRI acquisition. In summary, ECHO offers a ‘plug and play’ solution to capture artifact-free electrophysiological data without the need of expensive amplifiers or synchronization hardware which require physical connection to the MRI scanner. This device is expected to make multimodal imaging more accessible and be applied for a broad range of fMRI studies in both the research and clinical fields

Extended-Use ECG Monitor

In this thesis, a prototype ECG monitor was developed that is integrated into an elastic shirt and takes a 3-lead ECG for over 5 days. The high-quality measurements can be used to identify markers indicative of various detrimental heart conditions. Measurements recorded by the device are encrypted and stored onto a micro-SD card. Current Holter monitors are expensive and have functional lives less than 48 hours; however, extended duration monitoring has been proven more useful in diagnosis. The device designed demonstrates that ECG measurements can be taken over longer durations without sacrificing quality, comfort, or device cost

Inexpensive, Portable, Smartphone-Based 12-Lead Electrocardiogram

The electronics industry is constantly focusing on creating cheaper, more portable electronics that integrate with smartphone technology and the increasing demand for affordable and effective medical instrumentation. This Major Qualifying Project aims to complete preliminary development of an inexpensive, accurate, portable, Bluetooth 12-lead electrocardiogram (ECG) that interfaces with any Android smartphone. The current market lacks in mobile ECGs for portable devices to use clinically and other portable ECGs are extremely expensive. This project aims to use a compact analog front-end board, an embedded processor with Bluetooth adapter, and generic Android smartphone to revolutionize the way the healthcare industry envisions portable 12-lead ECGs

Design and Implementation of Wireless Point-Of-Care Health Monitoring Systems: Diagnosis For Sleep Disorders and Cardiovascular Diseases

Chronic sleep disorders are present in 40 million people in the United States. More than 25 million people remain undiagnosed and untreated, which accounts for over $22 billion in unnecessary healthcare costs. In addition, another major chronic disease is the heart diseases which cause 23.8% of the deaths in the United States. Thus, there is a need for a low cost, reliable, and ubiquitous patient monitoring system. A remote point-of-care system can satisfy this need by providing real time monitoring of the patient\u27s health condition at remote places. However, the currently available POC systems have some drawbacks; the fixed number of physiological channels and lack of real time monitoring. In this dissertation, several remote POC systems are reported to diagnose sleep disorders and cardiovascular diseases to overcome the drawbacks of the current systems. First, two types of remote POC systems were developed for sleep disorders. One was designed with ZigBee and Wi-Fi network, which provides increase/decrease the number of physiological channels flexibly by using ZigBee star network. It also supports the remote real-time monitoring by extending WPAN to WLAN with combination of two wireless communication topologies, ZigBee and Wi-Fi. The other system was designed with GSM/WCDMA network, which removes the restriction of testing places and provides remote real-time monitoring in the true sense of the word. Second, a fully wearable textile integrated real-time ECG acquisition system for football players was developed to prevent sudden cardiac death. To reduce power consumption, adaptive RF output power control was implemented based on RSSI and the power consumption was reduced up to 20%. Third, as an application of measuring physiological signals, a wireless brain machine interface by using the extracted features of EOG and EEG was implemented to control the movement of a robot. The acceleration/deceleration of the robot is controlled based on the attention level from EEG. The left/right motion of eyeballs of EOG is used to control the direction of the robot. The accuracy rate was about 95%. These kinds of health monitoring systems can reduce the exponentially increasing healthcare costs and cater the most important healthcare needs of the society

Mini PCB Input Sensor and Display Circuits

The objective of this MQP was to use analog and other design techniques to build a circuit using one of Analog Device\u27s new IC\u27s. After much thought, research, and collaboration, the project chosen was the Mini PCB Input Sensor and Display Circuits . This project has two parts: the first being a small, inexpensive, and portable circuit designed to be distributed to students who attend WPI\u27s ECE undergraduate recruitment open houses. The second phase is a more complex version of the portable circuit, and would allow the user to connect to a PC via USB and display the output of the various sensors on a computer monitor. This paper guides the reader through the design process as seen through the eyes of the designers, detailing the unique processes considered to reach the final outcome

Multivariable measurement system based on a Blood Pressure measuring device

This Master's thesis describes the development of a multivariable acquisition system based in a sphygmomanometer format. The resulting device can acquire and plot an electrocardiogram with different configurations of electrodes. Moreover, the system is able to acquire the temperature of a person, its heart rate and their oxygen saturation. This information will be shown through a screen and the data will be shared to other devices via Bluetooth. The scope of the project includes the component selection of the system, hardware integration, development of the firmware in Python and the validation of all the functionalities

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

A Multi-Sensor Platform for Microcurrent Skin Stimulation during Slow Wave Sleep

Insu cient and low quality sleep is related to several health issues and social outcomes. Regular sleep study conducted in a sleep laboratory is impractical and expensive. As a result, miniature and non-invasive sleep monitoring devices provide an accessible sleep data. Though not as accurate as polysomnography, these devices provide useful data to the subject by tracking sleep patterns regularly. On the other hand, proactive improvement of sleep quality has been limited to pharmacological solutions and cranial electrotherapy stimulation. An alternative approach and a potential solution to sleep deprivation is a non-pharmacological technique which involves the application of micro-current electrical stimulation on the palm during Slow Wave Sleep (SWS). This thesis presents the development of a miniature device for SWS detection and electrocutaneous stimulation. Several sensors are embedded in the prototype device to measure physiological data such as body movement, electrodermal activity, heart rate, and skin and ambient temperature. Furthermore, the prototype device provides local storage and wireless transfer for data acquisition. The quality of the sensor data during sleep are discussed in this thesis. For future work, the results of this thesis shall be the used as a baseline to develop a more re ned prototype for clinical trials in sleep laboratories