Concept Design Of A Labview-MySQL Data Warehouse For Digital Recording of ECG-EMG

To align with the recommendations set forth by the World Health Organization regarding eHealth, this study aims to develop a concept design of a suitable data warehouse for healthcare using electronic medical recording of ECG-EMG. This concept design once implemented aims to assist health care providers in improving patient’s healthcare data management. The system consists of three main layer: data source layer; software application layer; and data service database layer. The data source layer consists of patient profile, patient consultation, and patient test. Patient test was conducted using Olimex ECG-EMG shield and NI myDAQ module. The software application layer, developed using Labview 2012 is the main user interface. It allows users to access and store data into the database. The data service database layer is the main data warehouse, developed using MySQL with stored procedures. Five male respondents between 22-44 years old have been identified as respondents for the ECG-EMG test. Validation of the ECG test results was done by a licensed physician. After conducting ECG-EMG tests on five respondents and duly validated by a licensed physician, the researchers have proven that this study entitled Concept Design of a Labview-MySQL Data Warehouse for Digital Recording of ECG-EMG can be used for electronic medical recording

Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras

This paper compares and explores the performance of both mobile device camera and laptop camera as convenient tool for capturing images for non-invasive detection of Diabetes Mellitus (DM) using facial block texture features. Participants within age bracket 20 to 79 years old were chosen for the dataset. 12mp and 7mp mobile cameras, and a laptop camera were used to take the photo under normal lighting condition. Extracted facial blocks were classified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). 100 images were captured, preprocessed, filtered using Gabor, and iterated. Performance of the system was measured in terms of accuracy, specificity, and sensitivity. Best performance of 96.7% accuracy, 100% sensitivity, and 93% specificity were achieved from 12mp back camera using SVM with 100 images.Comment: 11 pages, 5 figures, 3 tables, conferenc

Construction of a Repeatable Framework for Prostate Cancer Lesion Binary Semantic Segmentation using Convolutional Neural Networks

Prostate cancer is the 3rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con-volutional neural network has been used in multiple studies to ob-jectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and validate the results. In this study, we executed a repeatable framework for segmenting prostate cancer lesions using annotated apparent diffusion coefficient maps from the QIN-PROSTATE-Repeatability dataset — a publicly available dataset that includes multiparametric MRI images of 15 patients that are confirmed or suspected of prostate cancer with two studies each. We used a main architecture of U-Net with batch normalization tested with different encoders, varying data image augmentation combinations, and hyperparameters adopted from various published frameworks to validate which combination of parameters work best for this dataset. The best performing framework was able to achieve a Dice score of 0.47 (0.44-0.49) which is comparable to previously published studies. The results from this study can be objectively compared and improved with further studies whereas this was previously not possible

3D-Printed Hand Controlled by Arm Gestures to Verify the Robustness and Reliability of a Low Cost Surface Electromyography System

The study focuses on the development of a low-cost surface electromyography and 3D-printed hand gesture-recognition system. The complete system captures four (4) channels of EMG data through sEMG amplifier circuits interfaced to an Arduino prototyping board. This data is sent to a workstation wherein the graphical user interface shows the pre-processed signal. The gestures are used as control for the movements of the 3D-printed arm

Real-time Monitoring of the Semiconductor Wirebond Interconnection Process for Production Yield and Quality Improvement

The electronics industry in the Philippine is the largest contributor to the manufacturing sector within the country. The Philippines is also considered the fastest growing economy in the region where its electronics products are among its top exports. The manufacturing of new electronics devices that comes with the emerging technologies translate to new processes that require new equipment that poses challenges to the industry. Thus, innovative solutions are developed by the engineers to improve and increase the production yield. This study presents the development of a monitoring system that is interfaced to an existing wirebonding machine as a means of improving production yield. The system detects and records the bouncing of the “area under bond” which is identified as a major cause of product rejection. The Arduino-based microcontroller takes the U/SG (Ultrasonic Generator/Gain) signals from the machine. The Raspberry pi monitors the real-time signal provided by the microcontroller and compares the signal from the database module with trained signals. Tests run show that the system can detect the signals and present it in a “.csv” file format. Twenty (20) units of a specific electronic device were tested to identify between good and defective device

FPGA Implementation of a Telecommunications Trainer System

Field programmable gate arrays (FPGAs) have been used in a wide range of applications including the field of telecommunications. This paper presents the use of FPGAs in the implementation of both analog and digital modulation that includes amplitude modulation, frequency modulation, phase modulation, pulse code modulation, pulse width modulation, pulse position modulation, pulse amplitude modulation, delta modulation, amplitude shift keying, frequency shift keying, phase shift keying, time division multiplexing and different encoding techniques like non-return-to-zero line code, non-return-to-zero mark line code, non-return to zero inversion line code, Unipolar return-to-zero line code, bipolar return-to-zero line code, alternate mark inversion line code, and Manchester line code. Moreover, an FPGA can be designed to emulate a particular device like an oscilloscope, a function generator, or the like. This paper describes the capability of an FPGA to internally generate a low frequency input signal and through the use of a VGA port, it is able to display the signals in an output device. However, the use of FPGAs is not limited to the aforementioned applications because of its reconfigurability and reprogrammability

Throughput and Power Consumption Comparisons of Zigbee-based and ISM-based Implementations of WSAN

Wireless sensor and actuator networks have expanding applications which requires better throughput, power efficiency and cost effectiveness. This study intends to contribute to the growing pool of knowledge on WSAN especially in the design for novel applications such as image or video over WSANs, and solar energy and RF energy harvesting for the WSAN nodes. Two basic scalable wireless sensor and actuator networks were implemented and characterized in terms of throughput and power consumption. The two WSANs are the Zigbee-based WSAN which is based on the IEEE 802.15.4 protocol, and the ISM-based Zigbee which makes use of the industrial, scientific and medical (ISM) radio bands. The star topology was used for both WSAN implementations. The throughput is quantified with varied factors including distance from node to node, obstructions in between nodes and cochannel interference. As distance and obstructions between nodes are increased, the throughput for both networks decreases with varying degrees. Co-channel interference is also considered. The ISM-based WSAN network is weak in dealing with co-channel interference and error rate as compared to the Zigbee-based WSAN, thus requiring it to have a better data encryption. Power consumption is generally larger for the ISM-based WSAN as compared to its Zigbee-based counterpart. However, the ISM-based nodes consume the same power even up to a few hundreds of meters distance and are thus practical for covering large distances. Therefore, the Zigbee-based WSAN system is more appropriate for closed environment, such as in room automation and home automation applications where distance from node to node is relatively shorter. The ISMbased WSAN prototype, on the other hand, can be developed further for applications in larger areas such as deployment in fields and cities, since transmission is not generally limited by distance and obstructions

Comparative Analysis of ML algorithms for Predictive Prenatal Monitoring

Despite the rapid advancements in prenatal health care services a lot of low-income sectors are experiencing high fetal mortality rates because of inaccessible prenatal health services. The reasons include financial incapability, pregnant women residing in isolated regions cannot access reliable healthcare services, and insufficient healthcare equipments in certain areas. To assess the shortcomings of the current prevalent methods, this study proposes an accurate non-invasive process of prenatal health care assessment by using a trained machine learning algorithm in a telemedicine setup. This setup uses a mobile app for patients and doctors connected to a cloud storage database where the patient information is stored. The predictive model would then be able to predict whether a patient is a high-risk or low-risk pregnancy based on the patient information inputted in the app. The Machine Learning algorithms to be compared are Random Forest Decision Tree, Decision Tree, $\mathbf{K}$ -nearest neighbor, and SVM. After pre-processing the dataset, the predictive model was created by inputting the dataset of patient information to multiple machine learning algorithms and assessing their performance parameters. Based on the testing results, the preferred algorithm to be used is the Random Decision Tree Algorithm which had better overall performance than the previous model of Bautista and Quiwa. The study showed the further potential of the Machine Learning algorithm as a healthcare tool as data can now be easily attained using current technologies. Further improvement with the telemedicine setup could aid women who do not have sufficient access to healthcare services

Stress Detection in Video Feed: Utilizing Facial Action Units as Indicators in Various Machine Learning Algorithms

Stress has always been present in our daily lives. This has and always been essential in helping us grow. Yet, once stress becomes too much to handle and exhausts an individual to a point where there is no space for recovery, this stress may develop into the chronic stage. This harmful state of stress may lead to vulnerabilities in physical and mental health. It can also affect one\u27s quality of life and can hinder productivity in their everyday tasks and responsibilities in school or at work. Multiple studies have been done to detect stress by gathering physiological signals but these invasive measures may be affected by multiple factors. This research aimed to detect stress using a non-invasive measure of detecting facial actions units in the video setting. This was done by taking a recorded footage of the participant while answering an arithmetic test in a time-limited and competitive environment. Stress levels were validated through the participant\u27s own evaluation of how stressful the test was. Extraction of the facial action units was done by utilizing the OpenFace 2.0 interface. The following machine learning algorithms were used to classify the stress levels: multiple linear regression, support vector machine. Classifier performance was measured through accuracy and F1 score and found that random forest model performed the best in the classification using overall data and person specific data

Automated lung auscultation identification for mobile health systems using machine learning

An efficient classification system that aids in the computerized auscultation process was developed. A database of digital lung sounds was created from recorded lung sounds from anonymous patients using mobile application and digital stethoscopes. Efficiency of different classification algorithms to the dataset was tested, and their processing time was reduced up to 80.15% when applied with Principal Component Analysis (PCA). Among the six classification algorithms used, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) are more reliable to use in this dataset with a precision of 100% and 99.00%, respectively