QNRF Grants

Diabetes is one of the rapidly increasing diseases in Qatar and around the world that causes one of the highest numbers of mortalities [1]. 19% of Qatar's population between the ages of 20 and 79 suffered from diabetes as of 2018 while this number is estimated to double by 2035 [2]. Globally, this number is predicted to rise to 55% by 2035. Diabetic sensorimotor polyneuropathy (DSPN) is a key consequence of diabetes mellitus which is also the most common and costly complication, involving sensory, motor, and autonomic nerves in both type 1 (T1D) and type 2 (T2D) diabetes and can affect 50-90% of diabetic patients [3, 4]. DSPN can have a negative impact on the gait of patients with diabetes [5] as there is close connection between DSPN and anomalies in gait with an augmented risk of falls, particularly in older diabetic patients. Besides, DSPN is also an early indicator for non-healing diabetic wounds and diabetic foot ulcers, which account for one of the most common complications of diabetes, leading to increased healthcare cost, decreased quality of life, infections, amputations, and death. Early detection and better classification tools for DSPN can allow correct diagnosis and treatment of painful diabetic neuropathy as well as timely intervention to prevent foot ulceration, amputation and other diabetic complications. 

It has been recently shown using the pioneering imaging technique of corneal confocal microscopy [6] that remarkable abnormalities exist in gait, in not only patients with diabetes [7], but also in patients having reduced glucose tolerance and pre-diabetic symptoms [8]. These abnormalities have been linked to the severity of small fiber neuropathy, suggesting that alterations in small fibers and motor function and gait occur at very early stages of diabetes and identification of these alterations will allow greater control of risk factors to limit nerve damage and to apply interventions such as vitamin D replacement and exercises to improve gait and limit falls. The wide prevalence of DSPN and its projected increase in the near future has further necessitated a need for development of better diagnostic tools for early and/or better detection of DSPN.

In this project, we propose to develop a novel contactless wearable electromyogram (EMG) system with an associated graphical user-interface (GUI) for early, reliable, and robust detection of DSPN, stratification of severity and assessment of the functional consequences (Figure 1). The project will start with the development of a contactless capacitive electrode for the acquisition of clean EMG signals from lower-limb muscles and an adaptive neuro-fuzzy inference system (ANFIS) model will be trained utilizing the database created in the project. The ANFIS will be implemented in a host system (personal computer) for the diagnosis and stratification of DSPN severity. The system comprises of two subsystems: i) a contactless wearable capacitive sensor-based EMG signal acquisition and conditioning system; and ii) an adaptive digital filter with real-time signal processing and ANFIS classification (host system). The vastus lateralis (VL), Gastrocnemius Lateralis (GL), tibialis anterior (TA), and gastrocnemius medialis (GM) will be acquired by the contactless EMG system during several complete gait cycles when the patients will be instructed to walk at a self-selected rhythm on a 5m walkway of a portable force plate system. The vertical ground reaction force (vGRF) generated throughout several full gait cycles will be captured in the host system in synchrony to the four muscle EMG signals from wearable to identify the onset of muscle activation. The real-time adaptive digital filter will be the front-end of the classifier host subsystem to provide the dynamic filtering solution throughout the EMG signal measurement. The EMG signals’ linear envelopes curve will be produced after the offset removal, full-wave rectification, moving-average low-pass filtering, normalization by the mean of the EMG signal, and segmented to individual muscle activation in reference to vGRF data. The highest peaks and the delay to reach their peaks for the four different muscles, and inter-muscle activation parameter (co-activation index) will be calculated as EMG variables. The clinical gold-standard for DSPN severity detection technique, nerve conduction study (NCS) will be used for labeling the subject groups while creating the database. The severity of DSPN will be classified as: absent, mild, moderate and severe. The ANFIS will be trained using EMG variables (as inputs) to classify the subjects into 4 DSPN classes. The performance evaluation of the contactless ANFIS-EMG system will be carried out by comparing its classification accuracy with the NCS based DSPN stratification. We believe that the proposed system for real time assessment of DSPN will enable quantification of neuromuscular function to enhance the healthcare of diabetic patients in relation to identifying high-risk individuals to improve their risk factors and to target them for exercise to improve strength and gait as well as vitamin D replacement strategies.

The project has four specific major aims:

Aim 1: To design and implement a contactless EMG system to generate clean cEMG signal.

Aim 2: To design and implement a real-time and fully-automatic ANFIS classifier implemented in the host system for DSPN severity classification during gait dynamics.

Aim 3: To create a cEMG and NCS database for patients with differing severity of DSPN.

Aim 4: To validate the effectiveness of the Contactless ANFIS-EMG system to stratify DSPN severity with the NCS.

Biomedical implants are used to improve the quality of function of jeopardized structures or structures that have undergone serious bone and joint deterioration in human bodies. These implants are artificial medical devices that are inserted directly into the body of a patient. Implants are used to substitute the diseased or lost biological structure in order to restore function and form to original performance. Therefore, implants assist in enhancing the quality of life and human being's longevity for patients whose structures have been jeopardized [1]. However, implants often face challenges related to biocompatibility, biostability, and suitability of the tribo-bio-mechanical properties. This often leads to loss of function of the implant, the consequences of which are discomfort and pain. Most commonly used implants are hip, knee, spinal substitutions and joint replacements. Joints in human body agonize due to degenerative diseases like arthritis, which results in loss of function or pain [2]. This kind of diseases cause bone mechanical properties degradation owing to lack of normal biological self-healing progression or disproportionate loading. It has been reported that about 20% of people with age higher than forty agonize because of degenerative diseases. In addition, the older population is increasing faster than expected. Solutions for these problems lie in the development of more suitable biomaterials for implants that closely resemble bone dentin, a natural

composite with strong, elastic, and self-repairing capabilities. Worldover, research has been, and is still being conducted, to provide new and improved implant materials that, besides good tribo-bio-mechanical properties, prevent bacterial adhesion and subsequent infection. To this end, a multidisciplinary research team will design and develop a new clinically relevant biomimetic nacre-like metal-oxide/polymer/ceramic composite for producing implants with suitable tribo-bio-mechanical properties. In addition, the research team will develop a new manufacturing process for fabricating the proposed implants for bone revision surgery retardation. Other critical considerations of implants are their useful lifetime before functional failure in the human body and their impact on the surrounding human tissue. Functional failure of the implant may be followed up by revision surgery, which is often painful and has a relatively low success rate. Lifetime of implants depend on (i) the type of materials used for implants, (ii) surgical techniques implemented, (iii) geometry of implant, (iv) physical activity of patient, and (v) age of the patient. Of major concern to implant recipients is the life cycle of implants, which is currently limited to the order of 10-15 years. This relatively short life can be attributed to implant wear, loosening, and misalignment, which often cause pain and discomfort to the patient. To evade implants loosening and long-term service to avoid revision surgery, a material with outstanding mixtures of low elastic modulus closer to that of bone dentin is a critical requirement. Consequently, the demand for new and improved implants has been growing worldwide. In addition, there is an inherent need for the new generation of implants that are safer, more reliable, smarter, and with a longer lifetime. The form and functionality requirements for implants may differ from patient to patient depending on the extent of damage. Appropriate shapes of implants along with proper biomechanical, wear and corrosion resistance assist in restoring the functionality of jeopardized structures. Hence, the concept of patient matched implants is worth revisiting. Consequently, the research team will design and develop techniques and options for patient matched implants for greater accuracy, shorter rehabilitation and overall reduction in costs. The research team will assess and investigate opportunities for producing cost-effective patient matched implants inclusive of age and patient activity. In order to avoid unexpected failure and unnoticed deterioration of implant, it is important to make provision for controlling and monitoring implants’ performance in real time. In this regard, the research team will investigate opportunities for monitoring (i) the status of the tribo-bio-mechanical interactions between implant and bone, (ii) the implant’s performance, in real time, and (iii) the impact of the implant on the surrounding biological tissue. This will help the research team to develop implants with embedded wireless sensors for real-time monitoring and control. Capability to collect information on the current state of an implant will help in predicting the quality and residual life of implants thereby improving the efficiency of assessing both implant status and human acceptance of implants. Realtime monitoring capabilities are envisaged to provide information that can be used to detect early failures, assess failure progressions and predict the mean time between undesirable failures the results of which can be used by surgeons to improve the quality of life for individuals with implants. The research team also plan to conduct both clinical and cytotoxic tests on implant samples. Developed implants will undergo both in-vitro and in-vivo tests to verify and validate the required attributes as well as to ensure the clinical relevancy of the implants. Implant embedded wireless sensors will help to transmit information regarding degradation, abnormal mobility and fissures. This information is expected to provide early warning signs (through user notification interfaces) for patients and concerned physicians. Such an innovation would lead to objective quality control of implants for the benefit of the patient.

Cardiac arrhythmia, an abnormality of heart rhythm can lead to increase morbidity and sudden cardiac death. Centers for Disease Control and Prevention (CDC) have estimated sudden cardiac death (SCD) rates at more than 600,000 per year [1]. About 80% of SCD, resulting from ventricular arrhythmias [2] and other arrhythmias including Atrial fibrillation/Atrial flutter resulted in 112,000 deaths in 2013 [3]. Electrocardiogram (ECG) of healthy individuals may have morphological variabilities / abnormalities at baseline [4] with variable prevalence. Similarly patient following myocardial infarction may develop persistent abnormal ECG changes.  Currently there are no robust solutions to detect abnormalities from the baseline of individual personal ECG, which may lead to arrhythmias.  The purpose of this project is to design a personalized health monitoring system that can potentially detect early occurrences of arrhythmias from a healthy individual’s ECG signal. We first aim to model the natural and common causes of arrhythmias in the signal domain as a degradation of normal ECG beats to become abnormal beats. Several novel modelling techniques, both analytical and machine learning-based methods will be investigated by the project-team as this is crucial for accurate synthesis of abnormal beats for a healthy person that may result from “any” potential cardiac arrhythmia. Using the degradation models, we shall be able to perform abnormal beat synthesis to generate potential abnormal beats from a person’s normal beats. Following this a personalized classifier will be trained and used in a real-time monitoring system that can alert the individual in advance whenever an abnormal ECG beat is detected. The Team will make use of their recently proposed 1D Adaptive Convolutional Neural Networks (CNNs) that achieved the state-of-the-art performance level with a minimal complexity for patient-specific ECG classification. All such off-line applications will be performed in a dedicated server that is located in the cloud. Once the personalized classifier is trained and ready, the server will upload it to the ECG monitoring device as a plug-in application. As a result, the proposed system will conveniently be used on a low-power wearable or mobile device in real time as an advance warning system individually dedicated to the device owner. We are targeting a very high detection accuracy level (i.e., >99%) for detection with a very low false alarm rate (i.e., <0.1%). We shall implement a prototype client-server architecture that can accomplish all the aforementioned objectives in real-time. Finally, through our international collaboration team that includes academics and medical practitioners, the proposed project work will truly be a frontier study and is expected to result in many advanced methods that will define the new state-of-the-art in the field.