查看更多>>摘要：Falls are a multi-factor problem that poses a serious risk to the elderly. Approximately, 60% of falls are caused by a number of known factors, including the environment., which accounts for approximately 25-45% of falling risk. Most of the remainder results from a lack of personal balance control. Falling can cause long-term disabilities in the elderly, sometimes resulting in lower quality of life, and is also associated with increased medical expenses and personal care costs. In this study, we developed a falling assessment system to evaluate and classify individuals into four graded falling risk groups. During the test, all subjects were required to wear a self-developed dynamic measurement system and to perform two balance tests: a "Timed Up and Go Test" and a "30-Second Chair Stand Test." We obtained 29 characteristic parameters from the data recorded during these tests. Next, we performed group classification. Eigenvalues were normalized, and a principal component. analysis (PCA) was performed. After identifying informative characteristic parameters, support vector machine (SVM) was used to classify individuals as members of one of the four falling risk groups. These included low-, moderate-, high-, and extreme-risk groups. Using unreduced data of the 29 characteristic parameters extracted from the two balance tests, the accuracy of the SVM classification in allocating individuals to the correct group was 97.5%. After PCA, the 29 characteristic parameters were reduced to eight. principal components, and the SVM classification method using these eight. principal components was 93.25%.
查看更多>>摘要：Slow transit constipation (STC) is usually accompanied by intestinal motility abnormalities. Although conventional anorectal manometry could record the pressure in the colon, most patients need preparation of intestinal tract. The intervention of catheter for monitoring the intestinal pressure also affects the clinical measurement. The pressure data collected by the conventional anorectal manometry cannot fully characterize the dynamic characteristics of the intestine. Thus, we aimed to obtain colonic pressure data under normal physiological conditions. Utilizing these data, we analyze the difference of colonic motility parameters between healthy control and patients with STC. A micro-electronic capsule made by ourselves was used to gather the subjects' intestinal pressure in their daily life. Several intestinal motility parameters were calculated from the pressure profile. The average energy of colonic pressure data in the STC group is higher than the healthy control group (HC: 259.95 vs. STC: 821.28). But the STC group has a lower average complexity of colonic motility (11C: 0.80 vs. STC: 0.64). About 81.25% of the colonic data from patients with STC could be identified by using slow transit constipation (SVM) classifier. Compared with health control, most colonic parameters of patients with STC are higher under the normal physiological conditions, but the complexity of colonic motility is lower in the STC group. The correct rate of colonic pressure recognition in the STC group is more than 80% by using SVM classifier.
查看更多>>摘要：Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support, Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEC scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals.
查看更多>>摘要：Colonoscopy allows doctors to check the abnormalities in the intestinal tract without any surgical operations. The major problem in the Computer-Aided Diagnosis (CAD) of colonoscopy images is the low illumination condition of the images. This study aims to provide an image enhancement method and feature extraction and classification techniques for detecting polyps in colonoscopy images. We propose a novel image enhancement method with a Pyramid Histogram of Oriented Gradients (PHOG) feature extractor to detect polyps in the colonoscopy images. The approach is evaluated across different classifiers, such as Multi-Layer Perceptron (MLP), Adaboost, Support Vector Machine (SVM), and Random Forest. The proposed method has been trained using the publicly available databases CVC ClinicDB and tested in ETIS Larib and CVC ColonDB. The proposed approach outperformed the existing state-of-the-art methods on both databases. The reliability of the classifiers performance was examined by comparing their F1 score, precision, F2 score, recall, and accuracy. PHOG with Random Forest classifier outperformed the existing methods in terms of recall of 97.95%, precision 98.46%. F1 score 98.20%, F2 score of 98.00%, and accuracy of 98.21% in the CVC-ColonDB. In the ETIS-LARIB dataset it attained a recall value of 96.83%, precision 98.65%, F1 score 97.73%, F2 score 98.59%, and accuracy of 97.75%. We observed that the proposed image enhancement method with PHOG feature extraction and the Random Forest classifier will help doctors to evaluate and analyze anomalies from colonoscopy data and make decisions quickly.
查看更多>>摘要：Objective: This study aims to characterize P and Ta wave of Modified Limb Lead (MLL) Electrocardiogram (ECG) in Normal Sinus Rhythm (NSR) and Atrioventricular Block (AVB). Methods: ECGs were recorded using MLL configuration from 100 NSR volunteers (mean age 31 years, 35 women) and 20 male AVB patients (mean age 72 years). Amplitudes and durations of P, Ta wave, and PTa Interval (PTaI) were measured, plotted, and analyzed for both the groups. Results: P-wave amplitudes were larger in AVB, and also P, Ta waves correlated significantly in both groups with higher correlation in AVB (NSR: r = 0.52; AVB: r = 0.75). Ta-wave duration (313 +/- 25.1ms) was longer than P-wave duration (96 +/- 9.3ms) in AVB patients and was opposite to P-wave polarity in all the leads. PP Interval (PPI) correlated significantly with P wave (NSR: r = 0.27; AVB: r = 0.78), Ta wave (r = 0.47; r = 0.83), PTaI (r = 0.51; r = 0.92), and corrected PTaI (r = 0.61; r = 0.67). Conclusion: P-wave right axis shift leads to the higher P-wave amplitude in AVB which may be due to the advancing age and atrial chamber enlargement. In NSR, the duration of observable Ta wave was longer than P wave, whereas in AVB, the Ta wave duration was 3-3.5 times longer than P wave.
查看更多>>摘要：The femoral prostheses experience versatile loading during the activities of daily living (ADL) and subsequently encounter a variety of stresses. This paper presents a detailed finite element analysis (FEA) of the femoral implant under transient loading. The distinct loading patterns corresponding to the most commonly occurring ADL are utilized for simulating the different scenarios. The CT reconstructed CAD model of the human femur bone assembled with a femoral implant is utilized for this study. The loading scenarios for walking, stair ascent, stair descent, standing up, sitting down, standing on one leg and knee bending are simulated by using the joint reaction forces and moments, corresponding to a body weight of 750 N, for the FEA. The results of this study are validated using a preliminary in-house built experimental setup comprising a fixture for a stainless steel femoral implant with sensors attached at three locations on the implant. The results indicate that the highest stresses are generated in case of the stair descent, stair ascent and standing on a single leg type of activities. These activities that generate high stresses on the implant surfaces are not suitable for the longevity of the implant and are therefore not advisable for post-operative patients.
查看更多>>摘要：Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGC16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.
查看更多>>摘要：Biomedical engineering (BME) is a multidisciplinary field, resulting in a heavy course load from different fields. We hypothesize that, the engineering curriculum be tailored according to the requirements of the BME profession. In this study, we focus on the teaching of the finite element modeling (FEM) technique by redesigning the course to address the needs of the BME profession by some custom-made changes to meet the unmet. needs. After the completion of the course, evaluation methods of the students were analyzed and detailed over a survey providing feedback from the students. The surveys were related to the teaching the theory of FEM, the laboratory sessions, and the project sessions. The survey results were evaluated using statistical methods. The Pearson correlation coefficient showed a linear agreement between theoretical and practical sessions indicating efficient blending of skills because of the custom-made changes. The survey analysis showed that the students were in favour of the changes, allowing them to be more resourceful and confident with their skills. The positive results indicate a positive attitude among the students towards their profession. As the course design addresses the needs of the profession allowing students to fit in better, the students might, follow their own profession after graduation. A wider follow-up study might be planned next to compare the results between who received tailor-designed courses and those who did not.
NSTL
World Scientific