Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring

The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectation-maximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study. The ECG signal data that has been predicted is then compared with the actual ECG data stored in the main controller memory. Root mean square error (RMSE) is calculated to measure system performance. The simulation was performed on 13 ECG waves, each of them has 1000 samples. The simulation results show that the EM method has a lower predictive error value than the RI method. The average RMSE for the EM and RI methods is 4.77 and 6.63, respectively. The proposed method is expected to be used in the case of multi-node ECG monitoring, especially in the ZigBee application to minimize errors

THE EFFECTIVENESS OF HEALTH EDUCATION THROUGH SMARTPHONE AND BOOKLET ON KNOWLEDGE AND ATTITUDE OF ADOLESENCE REPRODUCTIVE HEALTH

Adolescent related to reproductive health problems. The problem occurs because of adolescent had a lack of knowledge and attitudes about reproductive health. Smartphones was effective tools of education and it can improve knowledge and attitudes of teenagers, so the problem on adolescent reproductive health can be solved. This research analyze the differences and effect of health education through a smartphone and a booklet on the knowledge and attitudes of adolescents about reproductive health, also to analyze the factors that change knowledge and attitudes of adolescent after receiving health education from smartphone. This research was a mix method research that combines quantitative and qualitative research with concurent embedded design. Quantitative research used quasi-experiment design, conducted on 84 adolescent, divided in to two groups. Qualitative research conducted in 8 adolescents who received health education through the smartphone as an informant. Differences in knowledge and attitudes before and after health education through smartphones and booklets were analyzed with the Wilcoxon test. There was the differences between health education through smartphone and booklet on changed knowledge and attitudes of adolescents about reproductive health. The infleunces of health education through smartphone on knowledge and attitudes of adolescents about reproductive health is better than booklet (p <0.05). The factors that cause the adolescent knowledge and attitude changed after getting health education through smartphones are good content, simple language, the content is interesting, easy to understand, being a trend, easy to read, effective, easy to carry, easy to store, more privacy, easily stored, simple, easily accessible and the content was complete. Smartphone as effective tools of health education, it can improve knowledge and attitudes of adolescents about reproductive health. Keywords: Health Education, Smartphones, Booklets, Adolescent Reproductive Healt

Avoiding Machine Learning Becoming Pseudoscience in Biomedical Research

The use of machine learning harbours the promise of more accurate, unbiased future predictions than human beings on their own can ever be capable of. However, because existing data sets are always utilized, these calculations are extrapolations of the past and serve to reproduce prejudices embedded in the data. In turn, machine learning prediction result raises ethical and moral dilemmas. As mirrors of society, algorithms show the status quo, reinforce errors, and are subject to targeted influences – for good and the bad. This phenomenon makes machine learning viewed as pseudoscience. Besides the limitations, injustices, and oracle-like nature of these technologies, there are also questions about the nature of the opportunities and possibilities they offer. This article aims to discuss whether machine learning in biomedical research falls into pseudoscience based on Popper and Kuhn's perspective and four theories of truth using three study cases. The discussion result explains several conditions that must be fulfilled so that machine learning in biomedical does not fall into pseudoscienc

Reproducibility of Standing Posture for X-Ray Radiography: A Feasibility Study of the BalancAid with Healthy Young Subjects

Unreliable spinal X-ray radiography measurement due to standing postural variability can be minimized by using positional supports. In this study, we introduce a balancing device, named BalancAid, to position the patients in a reproducible position during spinal X-ray radiography. This study aimed to investigate the performance of healthy young subjects’ standing posture on the BalancAid compared to standing on the ground mimicking the standard X-rays posture in producing a reproducible posture for the spinal X-ray radiography. A study on the posture reproducibility measurement was performed by taking photographs of 20 healthy young subjects with good balance control standing on the BalancAid and the ground repeatedly within two consecutive days. We analyzed nine posterior–anterior (PA) and three lateral (LA) angles between lines through body marks placed in the positions of T3, T7, T12, L4 of the spine to confirm any translocations and movements between the first and second day measurements. No body marks repositioning was performed to avoid any error. Lin’s CCC test on all angles comparing both standing postures demonstrated that seven out of nine angles in PA view, and two out of three angles in LA view gave better reproducibility for standing on the BalancAid compared to standing on the ground. The PA angles concordance is on average better than that of the LA angles

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients

Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi

Hasil dari peneitian banyak digunakan dan dikembangkan pada aplikasi yang telah banyak dimanfaatkan pada kehidupan sehari-hari. Proses identifikasi bunyi menjadi salah satu penelitian yang banyak dilakukan. Identifikasi bunyi yang dilakukan oleh manusia berbeda satu sama lain. Misal pada suara detak jantung, pada pendengar umum, suara detak jantung tidak memiliki informasi apa pun terkait kesehatan, tapi jika suara detak jantung diperdengarkan pada ahli medik atau dokter, maka informasi yang diperoleh akan berbeda, dokter dapat mengidentifikasikan suara detak jantung dikaitkan dengan kondisi kesehatan jantung. Selain dalam bidang medis, bunyi juga dimanfaatkan pada aplikasi berbasis bunyi dan suara pada Smart Homes. Namun, sebelum mengkaji tentang aplikasi pada Smart Homes dan aplikasi lain maka akan dibahas beberapa teori dasar tentang bunyi dan suara, seperti: teori suara dan bunyi, noise pada data suara, serta ekstraksi ciri suara bunyi yang secara spesifik akan menjelaskan tentang Mel Frequency Cepstrum Coefficients (MFCC). Berdasarkan hasil kajian dapat dibuat kerangka kerja aplikasi yang dibuat. Kerangka kerja yang disusun merupakan kerangka kerja yang umum dilakukan pada aplikasi dan penelitian tentang penggunaan data suara dan bunyi. Selain itu kajian ini akan menjabarkan tentang lingkup penelitian bunyi dan suara yang telah banyak dilakukan. Melalui penjabaran tentang lingkup penelitian didapatkan peluang penelitian yang dapat dilakukan pada data bunyi dan suara serta tantangannya

Versatile and Low-Cost Fabrication of Modular Lock-and-Key Microfluidics for Integrated Connector Mixer Using a Stereolithography 3D Printing

We present a low-cost and simple method to fabricate a novel lock-and-key mixer microfluidics using an economic stereolithography (SLA) three-dimensional (3D) printer, which costs less than USD 400 for the investment. The proposed study is promising for a high throughput fabrication module, typically limited by conventional microfluidics fabrications, such as photolithography and polymer-casting methods. We demonstrate the novel modular lock-and-key mixer for the connector and its chamber modules with optimized parameters, such as exposure condition and printing orientation. In addition, the optimization of post-processing was performed to investigate the reliability of the fabricated hollow structures, which are fundamental to creating a fluidic channel or chamber. We found out that by using an inexpensive 3D printer, the fabricated resolution can be pushed down to 850 &micro;m and 550 &micro;m size for squared- and circled-shapes, respectively, by the gradual hollow structure, applying vertical printing orientation. These strategies opened up the possibility of developing straightforward microfluidics platforms that could replace conventional microfluidics mold fabrication methods, such as photolithography and milling, which are costly and time consuming. Considerably cheap commercial resin and its tiny volume employed for a single printing procedure significantly cut down the estimated fabrication cost to less than 50 cents USD/module. The simulation study unravels the prominent properties of the fabricated devices for biological fluid mixers, such as PBS, urine and plasma blood. This study is eminently prospective toward microfluidics application in clinical biosensing, where disposable, low-cost, high-throughput, and reproducible chips are highly required