الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
Abstract
Remote healthcare and telemedicine technology have witnessed a large and
rapid development in the last decade with the large development of information
and communication technology (ICT). Smart portable products can now be used
for monitoring of different medical signals of individuals to track the human
general health and to detect abnormalities within some organ functionality, and
also in the pre-diagnosis of various diseases. Certain diseases are usually
associated with changes in some physiological parameters in the human body such
as heart rate, oxygen saturation, respiration rate, respiration pattern, body
temperature, and blood pressure. The diagnosis of such diseases involves making
some checks in the hospital to measure how the physiological parameter readings
are close to the normal rates, and then determine the presence or absence of those
diseases. With remote health monitoring, while sitting in the comfortable home, a
patient can either monitor his own health, or pass the readings to a specialist to see
if certain precautions or actions should be taken.
There are two modes for health monitoring: local mode, where the patient
himself or some relatives can monitor the biomedical signs and related
measurements, and they can call the specialist in case of some readings outside the
normal ranges; and remote mode, which provides accessibility for the medical
specialist to monitor the patient’s readings, remotely. This provides fast response
and interactions from the health organization in case of emergency.
The main challenges encountered in healthcare systems are the necessity to
capture diverse signals for a complete picture regarding the human health, the
need for an efficient monitoring algorithm with minimum time complexity, the
need to secure the measured signals with appropriate encryption schemes to keep
the security and integrity for the patient health records, the need to transmit the
signals to a central point for processing or storage while keeping the encrypted
versions of the signals unaffected by the communication channel, the need for
efficient schemes to process the received encrypted medical signals for information retrieval, and the need for powerful computational resources that can
support the required computations.
In this thesis, we present different implementations for efficient, accurate, and
secure health monitoring schemes, adopting both local and remote health
monitoring modes. The proposed schemes provide reliable solutions to monitor
and track several biomedical aspects. This may help in early prediction or
diagnosis of different diseases associated with abnormalities of those aspects, and
also can be used in emergency cases, where the transmission of critical patient
data can make a significant impact on the patient life.
In the first approach, we propose a contactless, inexpensive, and fast breathing
rate and respiration pattern monitoring algorithm using video analysis and
computer vision techniques. In the proposed algorithm, the chest region, which
exhibits the intended region of interest (ROI), is automatically detected, and then,
a number of dominant corner points within the ROI are extracted and tracked to
adjust the boundaries of the ROI during frame processing. The breathing pattern is
extracted from the integral form of frames, which simplifies and speeds up the
calculations. The proposed algorithm is tested on 28 videos of sleeping-simulated
positions, and the results are compared with the manual visual inspection values.
In linear regression results, the determination coefficient (R2) is 0.961, which
demonstrates high agreement with reference measurements. In addition, the
Bland-Altman plot shows that almost all data points are within the 95% limits of
agreement. Moreover, the time complexity of the proposed algorithm, which
involves taking just a single point value from the integral form of the image, is
lower than that of traditional methods that circulate over a large number of points.
In other words, the proposed algorithm achieves O(1) fixed time complexity
compared to O(N2) for traditional methods. The average speed of processing is
enhanced by about 17.4%. Consequently, the proposed approach is promising
towards contactless, low-cost, and fast respiration rate monitoring.
The purpose of the second proposal is to present a new multi-function portable
health monitoring device that can help in the pre-diagnosis of various diseases. It
can be used to keep an eye on people we need to care about, while keeping them in their normal daily life. In addition, the proposed device presents a framework for
securing the measured signals by adopting the advanced encryption standard
(AES) algorithm, and transmitting them over the communication channel with WiFi technology to either a mobile application in a local mode or to the cloud storage
through an access point. A medical specialist can visualize the health records in
real time only after providing decryption credentials. The proposed device can be
used as a stand-alone medical device for all people. It consists mainly of different
types of medical sensors and a programmable microprocessor. Because of its small
size, it can be easily carried anywhere and used personally to monitor the health of
the person at any time. It is not restricted to be used at home only, but can be
carried easily in any place: the office, the school, the club, and others.
Consequently, the proposed device is expected to perform measurement of heart
rate (HR), measurement of blood oxygen saturation (SpO2), acquisition of
photoplethysmograms (PPG), acquisition of electrocardiograms (ECG),
measurement of body temperature, measurement of air temperature, measurement
of air humidity, encryption of measured signals using AES algorithm, and
transmission of measured values and the encrypted signals to a mobile or access
point for further processing. Moreover, it can send immediate notifications (Email
messages) to caregivers if some thresholds on measurements are exceeded.
Furthermore, the proposed system measurements are compared with commercially
available High Care medical product measurements. Results demonstrate that
measurements of the proposed system are within the 95% confidence interval, and
the determination coefficient (R2) is 0.983, which demonstrates the high accuracy
of the proposed system.
One of the biological signals captured from the proposed device is the PPG
signal, that is a biological signal, which describes the volumetric change of blood
flow in peripherals with heartbeats. We study the possibility of using the PPG
signal as a biometric to identify people based on the way their heart beats. Two
strategies are adopted in this thesis for human identification using the PPG signals.
The first strategy adopts the Mel-Frequency Cepstral Coefficients (MFCCs)
algorithm for feature extraction, and artificial neural networks (ANN) for classification of persons. A dataset of PPG signals for 35 healthy persons was collected
to test the performance of the proposed approach. Experimental results demonstrate
100% and 98.07% accuracy levels using the hold-out method and the 10-fold crossvalidation method, respectively.
Another innovative PPG-based human identification approach based on deep
learning is proposed. A convolutional neural network (CNN) is specifically
designed for the purpose of PPG signal classification. The proposed identification
approach is applied on images with and without an additive white Gaussian noise
(AWGN) effect. The simulation results reveal that the proposed approach achieves
an accuracy of 99.5% with spectrogram representations and 89.8% with just a 2D
image representation, in the absence of noise. In addition, this thesis discusses the
efficiency of denoising techniques such as wavelet, Savitzky-Golay and Kalman
filters when involved with the proposed approach. The simulation results prove
that wavelet denoising gives better performance among the discussed denoising
techniques.