Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.
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Scour monitoring is an important measurement process to determine the soil erosion level at the pillar of bridge. Image-based approach is attractive because it allows monitoring process to be conducted continuously without halting the flow of the water during experiment. Scour images provide abundance of information from a single source of camera sensor. However, this information may appear in different features, orientation, size and brightness. Therefore, it is important to detect and recognise features that are related to scour monitoring and filtered out irrelevant features like image noises and artefacts. This paper presents implementation of image processing techniques to extract various information from scour images. Image inpainting technique is used to separate information of scour level and scale markers into two different images. A proposed gradient of marginal histogram technique is used to detect the horizontal scale line markers and scour level. Backpropagation neural network is used to recognise scale text markers and convert the measurement of the scour level from a pixel unit into a centimetre unit. Interpolation techniques are used to connect scour points and delineate the boundary indicating the level of the scour. Time series acquisition of scour images allows observation of the temporal variation of the scour levels. Results show that the proposed approach achieved higher accuracy than existence method. This approach allows the detection of the scour level for even and uneven sediments, contributing to the high accurate results at the spatial and temporal measurements, thus potentially offering continuous scour monitoring solution.
samn ukm. my, iz, s d t~~~s com. Ln IntroductionDietary menu planning is a complicated process that researchers have tried to computerize since the early 1960s. The application of artificial intelligence (Al) techniques to such a process has also been put forward recently. However, the amount of common-sense knowledge involved and the complexity nature of the menu planning activities have yet produced encouraging results. This paper presents our effort in applying the case-based reasoning (CBR) technique to this so-called complex and complicated process. In this way, solution to a new case is based upon past cases stored in the system. Two similarity measures that will be used are the k-nearest neighbour and the symbolic indexing. BackgroundThere have been many attempts in the current health care practice to make health care more accessible, effective and efficient through the use of information technology such as the electronic patient record applications [1], the health information kiosks [2] and the medial diagnosis systems. One such attempt is a web-based system for dietary menu planning and management [3]. While such web-based systems are currently in existence, their focus is mainly to assist healthy individuals in calculating their calories intake and helping in monitoring the selection of menus based upon a pre-specified calories value. The difficulties and complexity of menu planning have also initiated researchers to adopt artificial intelligence technique as exhibited by systems such as Case-Based Menu Planner (CAMP) [4], Pattern Regulator for the Intelligent Selection of Menus (PRISM) [5] and CAMP Enhanced by Rules (CAMPER) [6]. CAMP employed the case-based reasoning (CBR) technique in suggesting menus to users. In this case, CAMP uses past menus which were compiled from reputable sources and modified as needed to ensure that they satisfy the RDIs, Dietary Guidelines of Americans and aesthetic standards. The menu generated by CAMP is based upon nutrient composition, type of servings, and the number of snacks.Although these systems prove to be helpful in some ways, they are by no means suitable for planning and designing the dietary needs and requirements for patients. This research therefore is an attempt to implement the case-based reasoning (CBR) in dietary menu planning for patients.
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