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

Activity-aware Stress Sensory System

Continuous stress monitoring may able to help analyzing and enhance the awareness of an individual on their stress patterns and provide more reliable data information for physicians in interventions. In the past years research, studies on mental stress sensory system were limited inside laboratory environment. However, excluding the effects of physical activity can be impractical while developing a wearable stress sensory system for daily use. In this project, effects of external factors from environment on Galvanic Skin Response (GSR) measurements and integration of several stress sensory system were studied. Electrocardiogram (ECG), GSR, and Activity Recognition System (ARS) were studied under different physical activities: sitting, standing, lying and walking. It is showed from the studies that an overall accuracy of 94.7% in ARS is achieved by using two sensor node system (at thigh and ankle each) which an improvement of 27.3% from using single sensor node system. It is further demonstrated that ARS could help improve in accuracy of wearable stress sensory system

A clinical patient vital signs parameter measurement, processing and predictive algorithm using ECG

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the modern clinical and healthcare setting, the electronic collection and analysis of patient related vital signs and parameters are a fundamental part of the relevant treatment plan and positive patient response. Modern analytical techniques combined with readily available computer software today allow for the near real time analysis of digitally acquired measurements. In the clinical context, this can directly relate to patient survival rates and treatment success. The processing of clinical parameters, especially the Electrocardiogram (ECG) in the critical care setting has changed little in recent years and the analytical processes have mostly been managed by highly trained and experienced cardiac specialists. Warning, detection and measurement techniques are focused on the post processing of events relying heavily on averaging and analogue filtering to accurately capture waveform morphologies and deviations. This Ph.D. research investigates an alternative and the possibility to analyse, in the digital domain, bio signals with a focus on the ECG to determine if the feasibility of bit by bit or near real time analysis is indeed possible but more so if the data captured has any significance in the analysis and presentation of the wave patterns in a patient monitoring environment. The research and experiments have shown the potential for the development of logical models that address both the detection and short term predication of possible follow-on events with a focus on Myocardial Ischemic (MI) and Infraction based deviations. The research has shown that real time waveform processing compared to traditional graph based analysis, is both accurate and has the potential to be of benefit to the clinician by detecting deviations and morphologies in a real time domain. This is a significant step forward and has the potential to embed years of clinical experience into the measurement processes of clinical devices, in real terms. Also, providing expert analytical and identification input electronically at the patient bedside. The global human population is testing the healthcare systems and care capabilities with the shortage of clinical and healthcare providers in ever decreasing coverage of treatment that can be provided. The research is a moderate step in further realizing this and aiding the caregiver by providing true and relevant information and data, which assists in the clinical decision process and ultimately improving the required standard of patient care

ANALYSIS OF ELECTROMYOGRAPHY (EMG) SIGNAL PROCESSING ATTRIBUTES

Electromyography (EMG) is a study of the muscular activity through analysis of electrical activity produced from muscles. This electrical activity is present as a raw signal as a signal is the result of neuromuscular activation that linked with muscle contraction. The most frequent techniques of signal recording are using surface and needle or wire electrode. The needle or wire electrode usually used by clinicians in clinical electromyography. This paper will concentrate on surface electromyography (SEMG) signal or also known as kinesiological signal. During recording and storing EMG data, several problems had to be encountered such as noise, motion artifacts and signal instability. Therefore, various filtering signal processing have been implemented to improve reliable signal for analysis. The purpose of this paper is to illustrate the best filtering methods that can be used for an EMG signal analysis. Then, the correlation between thermal images and EMG devices is implemented

Heart Rate Estimation During Physical Exercise Using Wrist-Type Ppg Sensors

Accurate heart rate monitoring during intense physical exercise is a challenging problem due to the high levels of motion artifacts (MA) in photoplethysmography (PPG) sensors. PPG is a non-invasive optical sensor that is being used in wearable devices to measure blood flow changes using the property of light reflection and absorption, allowing the extraction of vital signals such as the heart rate (HR). However, the sensor is susceptible to MA which increases during physical activity. This occurs since the frequency range of movement and HR overlaps, difficulting correct HR estimation. For this reason, MA removal has remained an active topic under research. Several approaches have been developed in the recent past and among these, a Kalman filter (KF) based approach showed promising results for an accurate estimation and tracking using PPG sensors. However, this previous tracker was demonstrated for a particular dataset, with manually tuned parameters. Moreover, such trackers do not account for the correct method for fusing data. Such a custom approach might not perform accurately in practical scenarios, where the amount of MA and the heart rate variability (HRV) depend on numerous, unpredictable factors. Thus, an approach to automatically tune the KF based on the Expectation-Maximization (EM) algorithm, with a measurement fusion approach is developed. The applicability of such a method is demonstrated using an open-source PPG database, as well as a developed synthetic generation tool that models PPG and accelerometer (ACC) signals during predetermined physical activities

The Design of Electro Cardiograph Signal Generator Using IC 14521 and IC 14017

ECG or Electrocardiograph is a tool to find out the electrical activity of the heart in patients. Electrocardiogram (ECG) is a graphic result records electrical potential resulting from by heartbeat, (Mervin J. Goldman, 1990). ECG recording very useful to know present certain hypertrophy, arrhythmia, pericarditis, for example potassium electrolyte disturbances. Tool this be named generator signal ECG. Tool the generator signal ECG functioning for produce the signal Electric heart in adult patient sand to check ECG error or no. So that it can ease technical electro medic in maintenance medical equipment periodically. The tool utilizes IC 14521 as oscillator or signal generator and using IC 14 017 as a shift register who has responsibility to remove from pin 1 to pin the other. The benefits of the ECG signal generating device so that technical electro medic easy and not difficult in check ECG without must using patients. So this tool as a form clone of the human heart. The tool such as a patient simulator. Based on the results of laboratory tests that have been done with the author concluded that, ECG signal generating device to function properly so that produce form of mock patient's heart signal that shown monitored oscilloscope in the waveform display P, Q, R, S, T

Lightweight Information Security Methods for Indoor Wireless Body Area Networks: from Channel Modeling to Secret Key Extraction

A group of wirelessly communicating sensors that are placed inside, on or around a human body constitute a Wireless Body Area Network (WBAN). Continuous monitoring of vital signs through WBANs have a potential to revolutionize current health care services by reducing the cost, improving accessibility, and facilitating medical diagnosis. However, sensitive nature of personal health data requires WBANs to integrate appropriate security methods and practices. As limited hardware resources make conventional security measures inadequate in a WBAN context, this work is focused on alternative techniques based on Wireless Physical Layer Security (WPLS). More specifically, we introduce a symbiosis of WPLS and Compressed Sensing to achieve security at the time of sampling. We successfully show how the proposed framework can be applied to electrocardiography data saving significant computational and memory resources. In the scenario when a WBAN Access Point can make use of diversity methods in the form of Switch-and-Stay Combining, we demonstrate that output Signal-to-Noise Ratio (SNR) and WPLS key extraction rate are optimized at different switching thresholds. Thus, the highest key rate may result in significant loss of output SNR. In addition, we also show that the past WBAN off-body channel models are insufficient when the user exhibits dynamic behavior. We propose a novel Rician based off-body channel model that can naturally reflect body motion by randomizing Rician factor K and considering small and large scale fading to be related. Another part of our investigation provides implications of user\u27s dynamic behavior on shared secret generation. In particular, we reveal that body shadowing causes negative correlation of the channel exposing legitimate participants to a security threat. This threat is analyzed from a qualitative and quantitative perspective of a practical secret key extraction algorithm

Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain

A primary factor for the success of machine learning is the quality of labeled training data. However, in many fields, labeled data can be costly, difficult, or even impossible to acquire. In comparison, computer simulation data can now be generated at a much higher abundance with a much lower cost. These simulation data could potentially solve the problem of data deficiency in many machine learning tasks. Nevertheless, due to model assumptions, simplifications and possible errors, there is always a discrepancy between simulated and real data. This discrepancy needs to be addressed when transferring the knowledge from simulation to real data. Furthermore, simulation data is always tied to specific settings of models parameters, many of which have a considerable range of variations yet not necessarily relevant to the machine learning task of interest. The knowledge extracted from simulation data must thus be generalizable across these parameter variations before being transferred. In this dissertation, we address the two outlined challenges in leveraging simulation data to overcome the shortage of labeled real data, . We do so in a clinical task of localizing the origin of ventricular activation from 12 lead electrocardiograms (ECGs), where the clinical ECG data with labeled sites of origin in the heart can only be invasively available. By adopting the concept of domain adaptation, we address the discrepancy between simulated and clinical ECG data by learning the shift between the two domains using a large amount of simulation data and a small amount of clinical data. By adopting the concept of domain generalization, we then address the reliance of simulated ECG data on patient-specific geometrical models by learning to generalize simulated ECG data across subjects, before transferring them to clinical data. Evaluated on in-vivo premature ventricular contraction (PVC) patients, we demonstrate the feasibility of utilizing a large number of offline simulated ECG datasets to enable the prediction of the origin of arrhythmia with only a small number of clinical ECG data on a new patient

Activity-aware Stress Sensory System

Continuous stress monitoring may able to help analyzing and enhance the awareness of an individual on their stress patterns and provide more reliable data information for physicians in interventions. In the past years research, studies on mental stress sensory system were limited inside laboratory environment. However, excluding the effects of physical activity can be impractical while developing a wearable stress sensory system for daily use. In this project, effects of external factors from environment on Galvanic Skin Response (GSR) measurements and integration of several stress sensory system were studied. Electrocardiogram (ECG), GSR, and Activity Recognition System (ARS) were studied under different physical activities: sitting, standing, lying and walking. It is showed from the studies that an overall accuracy of 94.7% in ARS is achieved by using two sensor node system (at thigh and ankle each) which an improvement of 27.3% from using single sensor node system. It is further demonstrated that ARS could help improve in accuracy of wearable stress sensory system

Evaluation of sensitivity and specificity of ECG left ventricular hypertrophy criteria in obese and hypertensive patients

Background: Left ventricular hypertrophy (LVH) is a well-known risk factor for cardiovascular events. Even though, there are many electrocardiographic (ECG) criteria for LVH, they still provide poor performance, especially among obese patients. The aim of this study was to examine the sensitivity and specificity in obese and nonobese patients, with obesity defined using body mass index (BMI), visceral fat level (VFATL) and waist hip ratio (WHR). Material and methods: Overall, 1722 patients were included in the study. All patients underwent complete physical examination, office blood pressure measurement, analysis of body composition, 12-lead ECG, M-mode two-dimensional echocardiography. Six standard ECG criteria for LVH were analyzed, including: Cornell voltage criteria, Cornell duration criteria, Sokolow- Lyon voltage criteria, Sokolow-Lyon product criteria, R I + S III and R wave of aVL. Sensitivity and specificity of those criteria was evaluated for patients with and without obesity. Transthoracic echocardiography was used as a reference method to detect LVH. Results: In obese patients, Cornell duration criteria showed the best performance and should be used in detecting LVH. Increased amount of adipose tissue and presence of obesity, defined by different indicators, decreased sensitivity and specificity values of ECG criteria; however, only several criteria showed statistical significance. Sokolov-Lyon voltage and Cornel voltage were evaluated to have good sensitivity in nonobese women patients, but their performance was insufficient in obese women. Conclusion: LVH should not be diagnosed using ECG criteria without assessment of patients obesity. Preferred parameter, from discussed in this study, to assess patients obesity is VFATL