Indonesian Applied Physics Letters

EMG Instrumentation Modeling and Feature Processing Based On Discrete Wavelet Transform

2024-05-31T22:10:48+07:00

Electromyography (EMG) instrumentation is essential in generating electrical signals from skeletal muscles. EMG sensors are helpful in various cases requiring the detection of human muscle contractions, neuromuscular disorders, and rehabilitation. EMG instrumentation is divided into two parts, namely, the analogue part and the digital part. The EMG instrumentation design comprises a digital-to-analog converter (DAC), instrumentation amplifier, filter, and analog-to-digital converter (ADC). Meanwhile, in digital signal processing adopting the Discrete Wavelet Transform (DWT) method, frequency analysis using DWT has proven superior. It is used in various research and has exceptionally detailed coefficient features for classifying neuromuscular disease signals. Therefore, this research aims to design analogue and digital EMG instrumentation and identify features in the form of detailed coefficients. The data used are two Physionet signals from the anterior tibialis body with myopathy and neuropathy disorders. The results obtained for EMG analogue instrumentation provide the expected results until they reach the filter component stage. The resulting signal forms a block in the filter component, different from the initial EMG signal. Meanwhile, the DWT decomposition results are of the Daubechies4 wavelet type with the highest level 17, which has a high detail coefficient at low frequencies, high dilation and the result of a mixture of neuropathy and myopathy EMG signals, or in other words, at low energies, this result is by the DWT theorem. Determining the efficiency of the DWT detailed coefficient feature requires further study with the same signal subject. The DWT features obtained can then be developed for various needs in EMG signal recognition.

The Implementation of Channel Area Thresholding in Early Detection System of Acute Respiratory Infection (ARI)

2024-05-31T22:06:34+07:00

Acute respiratory infections (ARI) are infectious diseases that affect both children and adults, particularly in the context of climate change. Bacteria are one of the causes of ARI. According to the government, the discovery of the bacteria that cause ARI is an indicator of successful management of infectious diseases. The current obstacle is the limited number of medical analysts, which results in longer microscopic examination times and requires a high level of objectivity. Therefore, a system for the early detection of ARI-causing bacteria was developed using digital image processing techniques, specifically channel area thresholding as one of the segmentation methods. This research employs four shape features for bacterial classification: the number of bacterial colonies, area, perimeter, and shape. The Naí¯ve Bayes intelligent system method is used for the classification process. The system had an accuracy rate of 86.84% in the classification of four types of bacteria: S. aureus, S. pneumoniae, C. diphteriae and M. tuberculosis

Review of Application YOLOv8 in Medical Imaging

2024-05-31T22:08:22+07:00

Deep learning has revolutionized medical imaging analysis, with YOLOv8 emerging as a promising tool for
various tasks like lesion detection, organ segmentation and disease classification. This review investigates YOLOv8's
applications across diverse medical imaging modalities (X-Ray, CT-Scan and MRI). We conducted a systematic literature
search across databases like Pubmed, ScienceDirect and IEEE to identify relevant studies evaluating YOLOv8's
performance in medical imaging analysis. YOLOv8 achieved high performance for meningioma and pituitary tumors
with and without data augmentation (precision >0.92, recall >0.90, mAP >0.93). Glioma detection showed lower
performance but still promising results (precision >0.86, recall >0.81, mAP >0.86). Breast cancer detection with SGD
optimizer yielded best performance with an average mAP of 0.87 for mass detection. The model achieved high accuracy
in detecting normal (mAP 0.939) and malignant lesions (mAP 0.911). YOLO v8 on Dental radiograph successfully
detected cavities, impacted teeth, fillings and implants (precision of >0.82, recall of >0.78 and F1-Score of >0.80). Lastly,
for lung disease classification, YOLOv8 achieved high accuracy (99.8% training and 90% validation) in classifying
normal, COVID-19, influenza and lung cancer disease. With the importance to improve clinical decision-making and
patient outcomes in healthcare, the YOLOv8 algorthm underscores the importance of pre-processing, augmentation and
optimization of key hyperparameters.

Detection of Throat Disorders Based on Thermal Image Using Digital Image Processing Methods

2024-05-31T22:09:44+07:00

Throat disorders are often considered trivial for some people, but if they are not treated immediately they can result in more severe conditions and require a longer time to cure this disorder. Objective, safe and comfortable detection of throat disorders is important because throat disorders are an indication of inflammation which, if not treated immediately, can have negative consequences. This research aims to detect throat disorders based on thermal images using digital image processing methods. Image capture was carried out with the same color pallete range on the camera, namely 33°C-38°C. The image obtained is then cropped in the ROI, then the image is threshold with a gray degree of 190. Pixels that have a gray degree above 190 are converted to white, while those below the threshold are converted to black. Next, the percentage of each white and black area is calculated compared to the total ROI area. If the percentage of white area is greater than 38% compared to the area of "‹"‹the throat then it is identified as having a throat disorder, whereas if the percentage of white is less than 38% then it is identified as not having a throat disorder. The detection program created provides an accuracy of 87.5% on sample data of 8 test data.

The Characteristics of Polyester Concrete with Local Sand of East Borneo as Filter

2024-05-31T22:21:31+07:00

Concrete is a mixture of coarse aggregate and fine aggregate mixed with water and cement as a binder and filler. The disadvantages of traditional concrete are that high water absorption causes low chemical resistance, low modulus of elasticity, low impact strength and a long hardening time to reach its maximum properties, namely 28 days. The solution to these shortcomings that is being developed for construction material applications is by using polymers as polymer concrete. In this research, polyester resin and sand aggregate were used as basic materials. Polyester resin is a type of thermosetting polymer that is widely used in various applications such as automotive parts, composites and construction because of its suitable processing characteristics and affordable price. Meanwhile, the sand used is local Kalimantan sand, where from the XRF and XRD test results, local Kalimantan sand is included in the silica sand type. This research varies the weight fraction of polyester resin used to determine its effect on polymer concrete characteristics such as porosity, water absorption, compressive strength, and macro observations. Variations in the polymer weight fraction used were 20%, 25% and 30%. Compressive strength testing was carried out at the age of 7 days of concrete. The results of the porosity test show that the average porosity of all variations is ± 0.5%. Meanwhile, the average value of water absorption for all fractions is 0.2%. And the highest average value of compressive strength in the 30% polyester resin weight fraction was 66.9 MPa. So it can be concluded that all variations meet SNI standards to become concrete materials.