A Patient-Adaptive Profiling Scheme for ECG Beat Classification

Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%

Multi-Class SVM Based on Sleep Stage Identification Using EEG Signal

Currently, sleep disorders are considered as one of the major human life issues. Human sleep is a regular state of rest for the body in which the eyes are not only usually closed, but also have several nervous centers being inactive; hence, rendering the person either partially or completely unconscious and making the brain a less complicated network. This paper introduces an efficient technique towards differentiating sleep stages to assist physicians in the diagnosis and treatment of related sleep disorders. The idea is based on easily implementable filters in any hardware device and feasible discriminating features of the Electroencephalogram (EEG) signal by employing the one-against-all method of the multiclass Support Vector machine (SVM) to recognize the sleep stages and identify if the acquired signal is corresponding to wake, stage1, stage2, stage3 or stage4.The experimental results on several subjects achieve 92% of classification accuracy of the proposed work. A comparison of our proposed technique with some recent available work in the literature also presents the high classification accuracy performance

A Review of Influenza Detection and Prediction Through Social Networking Sites

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.https://doi.org/10.1186/s12976-017-0074-

Secure Wireless Infrastructure Network Using Access Point Checking

Developments in computers, communication and networks has opened up the doors for wireless network evolution which enjoys attractive features such as dynamic communication and the ease of members to join the network. Improvements in wireless technology has increased the needed for more complicated security systems, where data security and protection represent main wireless networks features. In distributed systems, the use of networks and standard communication protocols facilitate data transmission between a terminal user and a computer - and between a computer and another computer. Network security measures the need to protect data during transmission. Clearly, wireless networks are less secure compared to wired networks. So, the most important question here is how to protect data transmission in wireless networks. In this work, we briefly glance at network classes and existing security mechanisms. We then propose our new access point checking algorithm to increase security over infrastructure wireless networks. The goal is to save the time consumed during message travel from one host to another in the network, while maintaining message security. We employ a checksum mechanism to enhance message integrity. In addition, access point (AP) will check the message and decide whether the message should be sent back to the original sender or not. Experimental results for different networking scenarios are provided to validate the system ability. Our technique outperforms traditional security mechanisms in terms of timing characteristics

Image-Based Risk Assessment Analysis for Glaucoma Determination

Glaucoma is the most common cause of blindness in the world, and it is known as the silent thief of vision because it can sneak up on any patient. However, the loss of vision from Glaucoma is preventable. Glaucoma is caused by the gradual increase of pressure in the eye which is known as Intraocular Pressure (IOP). While the pressure increases in the eye, different parts of the eye become affected until the eye parts are damaged. The eye vessels' sizes are so small that they easily become affected. Moreover, the pressure inside the eye pushes the lens affecting the size of the Pupil. Also, the pressure in the eye presses the optic nerve in the back of the eye causing damage to the nerve fibers. Over 90% of Glaucoma cases have no signs or symptoms because peripheral vision can be lost before a person's central vision is affected. The only way to prevent Glaucoma is by early detection. This research study calculates three features from the frontal eye image that can be used to assess the risk of Glaucoma. These features include redness of the sclera, red area percentage, and the Pupil size. The database used in the work contains 100 facial images that have been divided into 50 healthy cases and 50 non-healthy cases with high eye pressure. Once the features were extracted, a neural network classification is applied to obtain the status of the patients in terms of eye pressure

Real Time Sleep Detection System Using New Statistical Features of the Single EEG Channel

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. Many of the prior and current related studies use multiple EEG channels, and are based on 30s or 20s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, the aim of this work is to present a novel and efficient real time technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. First, we run our algorithm off line using the PhysioNet Sleep European Data Format (EDF) Database to classify six sleep stages. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Second, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance. Finally, we propose an effective EEG classification technique for detecting sleep to only prove that our algorithm is simple and works fast in real time in an efficient way using Neurosky Mindwave headset that gathers the user’s brain waves

Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps

Recent technological developments along with advances in smart healthcare have been rapidly changing the healthcare industry and improving outcomes for patients. To ensure reliable smartphone-based healthcare interfaces with high levels of efficacy, a system dynamics model with sustainability indicators is proposed. The focus of this paper is smartphone-based breathing monitoring systems that could possibly use breathing sounds as the data acquisition input. This can especially be useful for the self-testing procedure of the ongoing global COVID-19 crisis in which the lungs are attacked and breathing is affected. The method of investigation is based on a systems engineering approach using system dynamics modeling. In this paper, first, a causal model for a smartphone-based respiratory function monitoring is introduced. Then, a systems thinking approach is applied to propose a system dynamics model of the smartphone-based respiratory function monitoring system. The system dynamics model investigates the level of efficacy and sustainability of the system by studying the behavior of various factors of the system including patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded software and hardware breathing monitoring system design and performance metrics (e.g., accuracy, real-time response, etc.). The sustainability level is also studied through introducing various indicators that directly relate to the three pillars of sustainability. Various scenarios have been applied and tested on the proposed model. The results depict the dynamics of the model for the efficacy and sustainability of smartphone-based breathing monitoring systems. The proposed ideas provide a clear insight to envision sustainable and effective smartphone-based healthcare monitoring systems.https://doi.org/10.3390/su1212506

Detecting Malicious Behavior for the Sensors and Actuators Embedded in Medical Devices: A Hardware Approach

The goal of this study is to investigate a behavior-rule based technique for detecting the malicious behavior of the sensors and actuators embedded in medical devices such as Vital Sign Monitor (VSM), Patient Analgesic Control (PCA), Cardiac Device (CD), and Continuous Glaucous Monitor (CGM). First, a set of behavior rules for both malicious and normal behaviors are proposed. Second, a transformation methodology has been used to transfer the proposed set of behavior rules into a state machine. Finally, a Finite State Machine (FSM) has been built using Altera ModelSim and Quartus II toolset. The simulation and synthesis results using a Field Programmable Gate Array (FPGA) demonstrate that our FSM hardware model can effectively identify malicious behavior from normal behavior

Engage Students in Engineering – using Everyday Engineering Examples

Fifty percent of students entering engineering programs do not earn an engineering degree. Many students leave engineering because unsatisfactory experiences in introductory engineering courses in their first and second years. Improving student engagement through the use of everyday examples is one key ENGAGE strategy because research indicates that this strategy has a powerful impact upon students’ satisfaction with and perseverance in engineering. This project implemented Everyday Engineering Examples (E^3s) in four engineering classes to teach technical concepts through a ENGAGE E^3s mini-grant. This paper introduces ENGAGE project and E^3s and shares the benefits of our E^3s mini-grant experience and strategies of using the E^3s examples in engineering classes

SKINCure: An Innovative Smart Phone-Based Application to Assist in Melanoma Early Detection and Prevention

Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the infection; early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due to doctors’ subjectivity. This paper proposes an innovative and fully functional smart-phone based application to assist in melanoma early detection and prevention. The application has two major components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image database from Pedro Hispano Hospital for development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the normal, atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively