Search results for: cardiac patient

Authors: Enas M. F. El Houby, Marwa S. Hassan

Abstract:

Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.

Keywords: Associative Classification, Data mining, Decision tree, HCV, interferon.

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Authors: Y. A. Adla, R. Soubra, M. Kasab, M. O. Diab, A. Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals out of which 11 were chosen based on their Intraclass Correlation Coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, five features were introduced to the Linear Discriminant Analysis classifier and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90% respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Bruno B. Oliveira, Arnaldo P. Mourão, Teógenes A. da Silva

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The Brazilian legislation has only established diagnostic reference levels (DRLs) in terms of Multiple Scan Average Dose (MSAD) as a quality control parameter for computed tomography (CT) scanners. Compliance with DRLs can be verified by measuring the Computed Tomography Kerma Index (Ca,100) with a pencil ionization chamber or by obtaining the kerma distribution in CT scans with radiochromic films or rod shape lithium fluoride termoluminescent dosimeters (TLD-100). TL dosimeters were used to record kerma profiles and to determine MSAD values of a Bright Speed model GE CT scanner. Measurements were done with radiochromic films and TL dosimeters distributed in cylinders positioned in the center and in four peripheral bores of a standard polymethylmethacrylate (PMMA) body CT dosimetry phantom. Irradiations were done using a protocol for adult chest. The maximum values were found at the midpoint of the longitudinal axis. The MSAD values obtained with three dosimetric techniques were compared.

Keywords: Kerma profile, CT, MSAD, patient dosimetry

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Authors: Fatanah M. Suhaimi, J. Geoffrey Chase, Christopher G. Pretty, Rodney Elliott, Geoffrey M. Shaw

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Microcirculatory dysfunction is very common in sepsis and may results in organ failure and increased risk of death. Analyzing oxygen utilization can potentially assess microcirculation function of an individual. In this study, a modified pulse oximeter is used to extract information signals due to absorption of red (R) and infrared (IR) light. IR and R signal are related to the overall blood volume and reduced hemoglobin, respectively. Differences between these two signals thus represent the amount of oxygenated hemoglobin. Avascular occlusion test has been conducted on healthy individuals to validate the pulse oximeter concept. In this test, both R and IR signals rapidly changed according to the occlusion process. The pulse oximeter concept presented is capable of extracting valuable information to assess microcirculation condition. Implementing this concept on ICU patients has the potential to aid sepsis diagnosis and provide more accurate tracking of patient state and sepsis status.

Keywords: Microcirculation, sepsis, sepsis diagnosis, oxygen extraction.

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Authors: Saimir Heta, Ilma Robo, Nevila Alliu, Tea Meta

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Background: Plasma levels of inflammatory markers are the expression of the infectious wastes of existing periodontitis, as well as of existing inflammation everywhere in the body. Materials and Methods: The study consists of the clinical part of the measurement of inflammatory markers of 23 patients diagnosed with chronic periodontitis and the recording of parental periodontal parameters of patient periodontal status: hemorrhage index and probe values, before and 7-10 days after non-surgical periodontal treatment. Results: The level of fibrinogen drops according to the categorization of disease progression, active and passive, with the biggest % (18%-30%) at the fluctuation 10-20 mg/d. Fluctuations in fibrinogen level according to the age of patients in the range 0-10 mg/dL under 40 years and over 40 years was 13%-26%, in the range 10-20 mg/dL was 26%-22%, in the 20-40 mg/dL was 9%-4%. Conclusions: Non-surgical periodontal treatment significantly reduces the level of non-inflammatory markers in the blood. Oral health significantly reduces the potential source for periodontal bacteria, with the potential of promoting thromboembolism, through interaction between thrombocytes.

Keywords: Chronic periodontitis, atherosclerosis, risk factor, inflammatory markers.

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Authors: N. Mpofu, M. Sears

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In this paper, we propose a novel algorithm for delineating the endocardial wall from a human heart ultrasound scan. We assume that the gray levels in the ultrasound images are independent and identically distributed random variables with different Rician Inverse Gaussian (RiIG) distributions. Both synthetic and real clinical data will be used for testing the algorithm. Algorithm performance will be evaluated using the expert radiologist evaluation of a soft copy of an ultrasound scan during the scanning process and secondly, doctor’s conclusion after going through a printed copy of the same scan. Successful implementation of this algorithm should make it possible to differentiate normal from abnormal soft tissue and help disease identification, what stage the disease is in and how best to treat the patient. We hope that an automated system that uses this algorithm will be useful in public hospitals especially in Third World countries where problems such as shortage of skilled radiologists and shortage of ultrasound machines are common. These public hospitals are usually the first and last stop for most patients in these countries.

Keywords: Endorcardial Wall, Rician Inverse Distributions, Segmentation, Ultrasound Images.

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Authors: Natalia Constantinescu

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This cross-sectional study aims to explore the differences among adults with somatic symptom disorder (SSD) versus adults without SSD, in terms of attachment and emotion regulation strategies. A total sample of 80 participants (40 people with SSD and 40 healthy controls), aged 20-57 years old (M = 31.69, SD = 10.55), were recruited from institutions and online groups. They completed the Romanian version of the Experiences in Close Relationships Scale – Short Form (ECR-S), Regulation of Emotion Systems Survey (RESS), Patient Health Questionnaire-15 (PHQ-15) and Somatic Symptom Disorder – B Criteria Scale (SSD-12). The results indicate significant differences between the two groups in terms of attachment and emotion regulation strategies. Adults with SSD have a higher level of attachment anxiety and avoidance compared to the nonclinical group. Moreover, people with SSD are more prone to use rumination and suppression and less prone to use reevaluation compared to healthy people. Implications for SSD prevention and treatment are discussed.

Keywords: adult attachment, emotion regulation strategies, psychosomatic disorders, somatic symptom disorder

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Authors: Vikas Pandey, T. L. Joshy, Vyshak Vijayan, N. Babu

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A new and cost effective robotic device was designed for remote tele surgery using dual tone multi frequency technology (DTMF). Tele system with Dual Tone Multiple Frequency has a large capability in sending and receiving of data in hardware and software. The robot consists of DC motors for arm movements and it is controlled manually through a mobile phone through DTMF Technology. The system enables the surgeon from base station to send commands through mobile phone to the patient’s robotic system which includes two robotic arms that translate the input into actual instrument manipulation. A mobile phone attached to the microcontroller 8051 which can activate robot through relays. The Remote robot-assisted tele surgery eliminates geographic constraints for getting surgical expertise where it is needed and allows an expert surgeon to teach or proctor the performance of surgical technique by real-time intervention.

Keywords: Robot, Microcontroller, DTMF, Tele surgery.

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Authors: K. Bensafia, A. Mansour, G. Le Maillot, B. Clement, O. Reynet, P. Ariès, S. Haddab

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This paper presents a system designed for wireless acquisition, the recording of electrocardiogram (ECG) signals and the monitoring of the heart’s health during surgery. This wireless recording system allows us to visualize and monitor the state of the heart’s health during a surgery, even if the patient is moved from the operating theater to post anesthesia care unit. The acquired signal is transmitted via a Bluetooth unit to a PC where the data are displayed, stored and processed. To test the reliability of our system, a comparison between ECG signals processed by a conventional ECG monitoring system (Datex-Ohmeda) and by our wireless system is made. The comparison is based on the shape of the ECG signal, the duration of the QRS complex, the P and T waves, as well as the position of the ST segments with respect to the isoelectric line. The proposed system is presented and discussed. The results have confirmed that the use of Bluetooth during surgery does not affect the devices used and vice versa. Pre- and post-processing steps are briefly discussed. Experimental results are also provided.

Keywords: Electrocardiography, monitoring, surgery, wireless system.

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Authors: John Tolentino, Yong Seok Park

Abstract:

There are currently many people living with limb loss in the USA. The main causes for amputation can range from vascular disease, to trauma, or cancer. This number is expected increase over the next decade. Many patients have a single prosthetic for the first year but end up getting a second one to accommodate their changing physique. Afterwards, the prosthesis gets replaced every three to five years depending on how often it is used. This could cost the patient up to $500,000 throughout their lifetime. Complications do not end there, however. Due to the absence of nerves, it becomes more difficult to traverse terrain with a prosthetic. Moving on an incline or decline becomes difficult, thus curbs and stairs can be a challenge. Certain physical activities, such as cycling, could be even more strenuous. It will need to be relearned to accommodate for the change in weight, center of gravity, and transfer of energy from the leg to the pedal. The purpose of this research project is to develop a new, alternate below-knee cycling prosthetic using Dieter & Schmidt’s design process approach. It will be subjected to fatigue analysis under dynamic loading to observe the limitations as well as the strengths and weaknesses of the prosthetic. Benchmark comparisons will be made between existing prosthetics and the proposed one, examining the benefits and disadvantages. The resulting prosthetic will be 3D printed using acrylonitrile butadiene styrene (ABS) or polycarbonate (PC) plastic.

Keywords: 3D printing, cycling, prosthetic design, synthetic design.

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Authors: Amjad Al Shammari, Ashraf Ibrahim, Laila Seada

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Background and Objectives: Incidence of thyroid carcinoma has been increasing world-wide. In the present study, we evaluated diagnostic accuracy of Fine needle aspiration (FNA) and its efficiency in early detecting neoplastic lesions of thyroid gland over a 3-year period. Methods: Data have been retrieved from pathology files in King Khalid Hospital. For each patient, age, gender, FNA, site & size of nodule and final histopathologic diagnosis were recorded. Results: Study included 490 cases where 419 of them were female and 71 male. Male to female ratio was 1:6. Mean age was 43 years for males and 38 for females. Cases with confirmed histopathology were 131. In 101/131 (77.1%), concordance was found between FNA and histology. In 30/131 (22.9%), there was discrepancy in diagnosis. Total malignant cases were 43, out of which 14 (32.5%) were true positive and 29 (67.44%) were false negative. No false positive cases could be found in our series. Conclusion: FNA could diagnose benign nodules in all cases, however, in malignant cases, ultrasound findings have to be taken into consideration to avoid missing of a microcarcinoma in the contralateral lobe.

Keywords: FNA, hail, histopathology, thyroid.

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Authors: Aryana Collins Jackson, Elisabetta Bevacqua, Pierre De Loor, Ronan Querrec

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This paper presents a taxonomy of non-technical skills, communicative intentions, and behavior for an individual acting as a medical coordinator. In medical emergency situations, a leader among the group is imperative to both patient health and team emotional and mental health. Situational Leadership is used to make clear and easy-to-follow guidelines for behavior depending on circumstantial factors. Low-level leadership behaviors belonging to two different styles, directive and supporting, are identified from literature and are included in the proposed taxonomy. The high-level information in the taxonomy consists of the necessary non-technical skills belonging to a medical coordinator: situation awareness, decision making, task management, and teamwork. Finally, communicative intentions, dimensions, and functions are included. Thus this work brings high-level and low-level information - medical non-technical skills, communication capabilities, and leadership behavior - into a single versatile taxonomy of behavior.

Keywords: Medical, leadership styles, taxonomy, human behavior.

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Authors: Kasra Afsahi, Maryam Soheilifar, Noureddin Nakhostin Ansari

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COVID-19 has been associated with stroke and neurological complications. The patient was a 59-year-old male presented with sudden left hemiparesis and diplopia due to cavernous sinus thrombosis (CST) on 28/03/2020. The COVID-19 test was positive. Multislice computerized tomography (MSCT) showed ischemic infarction. He underwent surgical sinectomy 9 days after admission. Physiotherapy began for him on August 2020. Our game-based virtual reality (VR) technology developed for stroke patients was based on upper extremity exercises and function for stroke. After 6 weeks of VR therapy plus conventional physiotherapy exercises (18 sessions, three times per week, 60 minutes each session), there were significant improvements in Brunnstrom Motor Recovery Stage (from “4” to “5”), Fugl-Meyer Scale score of upper extremity section (from 49 to 54), and Modified Barthel Index (from 15 to 18). There were no adverse effects. This case with stroke post COVID-19 due to the CST showed the usefulness of VR therapy used as an adjunct to conventional physiotherapy in improving affected upper extremity.

Keywords: COVID-19, stroke, virtual reality, rehabilitation.

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Authors: Naima Nur, Safa Islam, Saeema Islam, Md. Faridul Alam

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The study addresses the increasing challenge of pediatric presumptive tuberculosis, emphasizing the need to understand the factors associated with it. The research aims to determine the proportion of presumptive TB and factors associated with it among suspected pediatric tuberculosis patients. A cross-sectional study was conducted at ICDDR-Bangladesh, collecting specimens from suspected pediatric patients and using logistic regression for data analysis. The study found a high proportion of presumptive TB (85.7%) but no statistically significant differences between presumptive and non-presumptive TB. Theoretical importance of the study highlights the importance of identifying factors associated with presumptive TB for better control and management strategies. Specimens were collected from 84 suspected pediatric patients diagnosed with TB based on clinical symptoms/radiological findings. Microbiological tests like smear-microscopy, culture, and GeneXpert were used to isolate presumptive TB and confirmed TB. The proportion of presumptive TB was 85.7% among suspected pediatric TB patients. Among various factors that were not found to be associated with the presumptive TB. The study concludes that despite a high proportion of presumptive TB, no significant differences were found between presumptive and non-presumptive TB cases.

Keywords: Presumptive tuberculosis, confirmed tuberculosis, patient's characteristics, diagnosis.

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Authors: L. Salhi, M. Talbi, A. Cherif

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This paper presents a new strategy of identification and classification of pathological voices using the hybrid method based on wavelet transform and neural networks. After speech acquisition from a patient, the speech signal is analysed in order to extract the acoustic parameters such as the pitch, the formants, Jitter, and shimmer. Obtained results will be compared to those normal and standard values thanks to a programmable database. Sounds are collected from normal people and patients, and then classified into two different categories. Speech data base is consists of several pathological and normal voices collected from the national hospital “Rabta-Tunis". Speech processing algorithm is conducted in a supervised mode for discrimination of normal and pathology voices and then for classification between neural and vocal pathologies (Parkinson, Alzheimer, laryngeal, dyslexia...). Several simulation results will be presented in function of the disease and will be compared with the clinical diagnosis in order to have an objective evaluation of the developed tool.

Keywords: Formants, Neural Networks, Pathological Voices, Pitch, Wavelet Transform.

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Authors: Aliza Lee, Mark Wilt, John Bonk, Scott Floyd, Bradley Hoffman, Karen Uchmanowicz

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Bipartite medial cuneiforms are relatively rare but may play a significant role in biomechanical and gait abnormalities. It is believed that a bipartite medial cuneiform may alter the available range of motion due to its larger morphological variant, thus limiting the metatarsal plantarflexion needed to achieve adequate hallux dorsiflexion for normal gait. Radiographic and clinical assessment were performed on two patients who reported with foot pain along the first ray. Both patients had visible bipartite medial cuneiforms on MRI. Using gait plate and Metascan ™ analysis, both were noted to have four measurements far beyond the expected range. Medial and lateral heel peak pressure, hallux peak pressure, and 1st metatarsal peak pressure were all noted to be increased. These measurements are believed to be increased due to the hindrance placed on the available ROM of the first ray by the increased size of the medial cuneiform. A larger patient population would be needed to fully understand this developmental anomaly.

Keywords: Bipartite medial cuneiforms, cuneiform, developmental anomaly, gait abnormality.

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Authors: Inas S. Khayal, Weiping Zhou, Jonathan Skinner

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Healthcare delivery systems around the world are in crisis. The need to improve health outcomes while decreasing healthcare costs have led to an imminent call to action to transform the healthcare delivery system. While Bioinformatics and Biomedical Engineering have primarily focused on biological level data and biomedical technology, there is clear evidence of the importance of the delivery of care on patient outcomes. Classic singular decomposition approaches from reductionist science are not capable of explaining complex systems. Approaches and methods from systems science and systems engineering are utilized to structure healthcare delivery system data. Specifically, systems architecture is used to develop a multi-scale and multi-dimensional characterization of the healthcare delivery system, defined here as the Healthcare Delivery System Knowledge Base. This paper is the first to contribute a new method of structuring and visualizing a multi-dimensional and multi-scale healthcare delivery system using systems architecture in order to better understand healthcare delivery.

Keywords: Health informatics, systems thinking, systems architecture, healthcare delivery system, data analytics.

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Authors: Ali Zifan, Panos Liatsis, Panagiotis Kantartzis, Manolis Gavaises, Nicos Karcanias, Demosthenes Katritsis

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We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes.

Keywords: Vessel enhancement, centerline extraction, symbiotic reconstruction.

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Authors: D. O. Omondi, M. K. Walingo, G. M. Mbagaya, L. O. A. Othuon

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Understanding patient factors related to physical activity behavior is important in the management of Type 2 Diabetes. This study applied the Theory of Planned Behavior model to understand physical activity behavior among sampled Type 2 diabetics in Kenya. The study was conducted within the diabetic clinic at Kisii Level 5 Hospital and adopted sequential mixed methods design beginning with qualitative phase and ending with quantitative phase. Qualitative data was analyzed using grounded theory analysis method. Structural equation modeling using maximum likelihood was used to analyze quantitative data. The common fit indices revealed that the theory of planned behavior fitted the data acceptably well among the Type 2 diabetes and within physical activity behavior {¤ç2 = 213, df = 84, n=230, p = .061, ¤ç2/df = 2.53; TLI = .97; CFI =.96; RMSEA (90CI) = .073(.029, .08)}. This theory proved to be useful in understanding physical activity behavior among Type 2 diabetics.

Keywords: Physical activity, Theory of Planned Behavior, Type2 diabetes, Kenya.

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Authors: Aki Sugano, Mika Ohta, Mineko Ikegami, Kenji Miura, Sayo Tsukamoto, Akihiro Ichinose, Toshiko Ohshima, Eiichi Maeda, Masako Matsuura, Yutaka Takao

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Along with the advances in medicine, providing medical information to individual patient is becoming more important. In Japan such information via Braille is hardly provided to blind and partially sighted people. Thus we are researching and developing a Web-based automatic translation program “eBraille" to translate Japanese text into Japanese Braille. First we analyzed the Japanese transcription rules to implement them on our program. We then added medical words to the dictionary of the program to improve its translation accuracy for medical text. Finally we examined the efficacy of statistical learning models (SLMs) for further increase of word segmentation accuracy in braille translation. As a result, eBraille had the highest translation accuracy in the comparison with other translation programs, improved the accuracy for medical text and is utilized to make hospital brochures in braille for outpatients and inpatients.

Keywords: Automatic Braille translation, Medical text, Partially sighted people.

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Authors: Elnaz Lashgari, Emel Demircan

Abstract:

Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: Electrocardiogram, manifold learning, Laplacian Eigenmaps, running pattern.

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Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Metabolic syndrome (MetS) is a severe health problem caused by morbid obesity, the severest form of obesity. The components of MetS are rather stable in adults. However, the diagnosis of MetS in morbid obese (MO) children still constitutes a matter of discussion. The aim of this study was to develop a formula, which facilitated the diagnosis of MetS in MO children and was capable of discriminating MO children with and without MetS findings. The study population comprised MO children. Age and sex-dependent body mass index (BMI) percentiles of the children were above 99. Increased blood pressure, elevated fasting blood glucose (FBG), elevated triglycerides (TRG) and/or decreased high density lipoprotein cholesterol (HDL-C) in addition to central obesity were listed as MetS components for each child. Two groups were constituted. In the first group, there were 42 MO children without MetS components. Second group was composed of 44 MO children with at least two MetS components. Anthropometric measurements including weight, height, waist and hip circumferences were performed during physical examination. BMI and homeostatic model assessment of insulin resistance (HOMA-IR) values were calculated. Informed consent forms were obtained from the parents of the children. Institutional Non-Interventional Clinical Studies Ethics Committee approved the study design. Routine biochemical analyses including FBG, insulin (INS), TRG, HDL-C were performed. The performance and the clinical utility of Diagnostic Obesity Notation Model Assessment Metabolic Syndrome Index (DONMA MetS index) [(INS/FBG)/(HDL-C/TRG)*100] was tested. Appropriate statistical tests were applied to the study data. p value smaller than 0.05 was defined as significant. MetS index values were 41.6 ± 5.1 in MO group and 104.4 ± 12.8 in MetS group. Corresponding values for HDL-C values were 54.5 ± 13.2 mg/dl and 44.2 ± 11.5 mg/dl. There was a statistically significant difference between the groups (p < 0.001). Upon evaluation of the correlations between MetS index and HDL-C values, a much stronger negative correlation was found in MetS group (r = -0.515; p = 0.001) in comparison with the correlation detected in MO group (r = -0.371; p = 0.016). From these findings, it was concluded that the statistical significance degree of the difference between MO and MetS groups was highly acceptable for this recently introduced MetS index. This was due to the involvement of all of the biochemically defined MetS components into the index. This is particularly important because each of these four parameters used in the formula is a cardiac risk factor. Aside from discriminating MO children with and without MetS findings, MetS index introduced in this study is important from the cardiovascular risk point of view in MetS group of children.

Keywords: Fasting blood glucose, high density lipoprotein cholesterol, insulin, metabolic syndrome, morbid obesity, triglycerides.

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Authors: Seung You Na, Joo Hyun Jung, Jin Young Kim, Mohammad AhangarKiasari

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There are various kinds of medical equipment which requires relatively accurate positional adjustments for successful treatment. However, patients tend to move without notice during a certain span of operations. Therefore, it is common practice that accompanying operators adjust the focus of the equipment. In this paper, tracking controllers for medical equipment are suggested to replace the operators. The tracking controllers use AHRS sensor information to recognize the movements of patients. Sensor fusion is applied to reducing the error magnitudes through linear Kalman filters. The image processing of optical markers is included to adjust the accumulation errors of gyroscope sensor data especially for yaw angles. The tracking controller reduces the positional errors between the current focus of a device and the target position on the body of a patient. Since the sensing frequencies of AHRS sensors are very high compared to the physical movements, the control performance is satisfactory. The typical applications are, for example, ESWT or rTMS, which have the error ranges of a few centimeters.

Keywords: AHRS, Sensor fusion, Tracking control, Position and posture.

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Authors: M. Mousavi-Daramoroudi, H. Yousefnia, F. Abbasi-Davani, S. Zolghadri

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Dosimetry is an indispensable and precious factor in patient treatment planning to minimize the absorbed dose in vital tissues. In this study, compartmental model was used in order to estimate the human absorbed dose of ¹⁷⁷Lu-DOTATOC from the biodistribution data in wild type rats. For this purpose, ¹⁷⁷Lu-DOTATOC was prepared under optimized conditions and its biodistribution was studied in male Syrian rats up to 168 h. Compartmental model was applied to mathematical description of the drug behaviour in tissue at different times. Dosimetric estimation of the complex was performed using radiation absorbed dose assessment resource (RADAR). The biodistribution data showed high accumulation in the adrenal and pancreas as the major expression sites for somatostatin receptor (SSTR). While kidneys as the major route of excretion receive 0.037 mSv/MBq, pancreas and adrenal also obtain 0.039 and 0.028 mSv/MBq. Due to the usage of this method, the points of accumulated activity data were enhanced, and further information of tissues uptake was collected that it will be followed by high (or improved) precision in dosimetric calculations.

Keywords: Compartmental modeling, human absorbed dose, 177Lu-DOTATOC, Syrian rats.

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Authors: Delly C. Lestari, Linosefa, Ardiana Kusumaningrum, Andi Yasmon, Anis Karuniawati

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The objective of this study is to determine the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) harboring mecA genes from screening isolates among intensive care unit (ICU) patients. All MRSA screening isolates from ICU’s patients of Cipto Mangunkusumo Hospital during 2011 and 2014 were included in this study. Identification and susceptibility test was performed using Vitek2 system (Biomereux®). PCR was conducted to characterize the SCCmec of S. aureus harboring the mecA gene on each isolate. Patient’s history of illness was traced through medical record. 24 isolates from 327 screening isolates were MRSA positive (7.3%). From PCR, we found 17 (70.8%) isolates carrying SCCmec type I, 3 (12.5%) isolates carrying SCCmec type III, and 2 (8.3%) isolates carrying SCCmec type IV. In conclusion, SCCmec type I is the most prevalent MRSA colonization among ICU patients in Cipto Mangunkusumo Hospital.

Keywords: MRSA, mecA genes, ICU, colonization.

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Authors: Florin Gorunescu

Abstract:

Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.

Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.

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Authors: Wei-Han Wang, Hsiang-Yu Yu, Mau-Sun Hua

Abstract:

Theory of Mind (ToM) refers to the ability to infer another’s mental state. With appropriate ToM, one can behave well in social interactions. A growing body of evidence has demonstrated that patients with temporal lobe epilepsy (TLE) may damage ToM by affecting on regions of the underlying neural network of ToM. However, the question of whether there is cerebral laterality for ToM functions remains open. This study aimed to examine whether there is cerebral lateralization for ToM abilities in TLE patients. Sixty-seven adult TLE patients and 30 matched healthy controls (HC) were recruited. Patients were classified into right (RTLE), left (LTLE), and bilateral (BTLE) TLE groups on the basis of a consensus panel review of their seizure semiology, EEG findings, and brain imaging results. All participants completed an intellectual test and four tasks measuring basic and advanced ToM. The results showed that, on all ToM tasks, (1) each patient group performed worse than HC; (2) there were no significant differences between LTLE and RTLE groups; and (3) the BTLE group performed the worst. It appears that the neural network responsible for ToM is distributed evenly between the cerebral hemispheres.

Keywords: Cerebral lateralization, social cognition, temporal lobe epilepsy, theory of mind.

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Authors: Mahdi Mazinani, S. D. Qanadli, Rahil Hosseini, Tim Ellis, Jamshid Dehmeshki

Abstract:

Segmentation and quantification of stenosis is an important task in assessing coronary artery disease. One of the main challenges is measuring the real diameter of curved vessels. Moreover, uncertainty in segmentation of different tissues in the narrow vessel is an important issue that affects accuracy. This paper proposes an algorithm to extract coronary arteries and measure the degree of stenosis. Markovian fuzzy clustering method is applied to model uncertainty arises from partial volume effect problem. The algorithm employs: segmentation, centreline extraction, estimation of orthogonal plane to centreline, measurement of the degree of stenosis. To evaluate the accuracy and reproducibility, the approach has been applied to a vascular phantom and the results are compared with real diameter. The results of 10 patient datasets have been visually judged by a qualified radiologist. The results reveal the superiority of the proposed method compared to the Conventional thresholding Method (CTM) on both datasets.

Keywords: 3D coronary artery tree extraction, segmentation, quantification, fuzzy clustering, and Markov random field

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

Abstract:

Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: Cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet.

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Authors: J. Alamelu Mangai, Satej Wagle, V. Santhosh Kumar

Abstract:

The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.

Keywords: Medical Image Mining, Data Mining, Feature Weighting, Association Rule Mining, k nearest neighbor classifier.

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