Sleep Apnea Identification using HRV Features of ECG Signals

Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features

Penggunaan Teknik Unsupervised Discretization pada Metode Naive Bayes dalam Menentukan Jurusan Siswa Madrasah Aliyah

Pemilihan jurusan bagi siswa merupakan langkah positif yang dilakukan untuk memfokuskan siswa sesuai dengan potensi yang dimiliki, hal ini dianggap penting karena dengan adanya jurusan, siswa diharapkan mampu mengembangkan kemampuan akademis sesuai bidang yang dikuasai. Pada penelitian sebelumnya, telah dilakukan pengujian dengan metode Naive Bayes yang bertujuan untuk mengkasifikasikan jurusan siswa bedasarkan kriteria yang menunjang dengan studi kasus pada siswa Madrasah Aliyah Swasta PAB 6 Helvetia, dan didapatkan hasil pengujian dari 100 data siswa dengan tingkat keakuratan 90%. pada penelitian ini, dilakukan optimalisasi metode yang digunakan sebelumnya dengan menerapkan teknik Unsupervised Discretization yang akan mentransformasikan kriteria numerik/kontinyu menjadi kriteria kategorikal dan mengeliminasi satu kriteria yang dianggap tidak mempengaruhi keakuratan hasil pengujian, dengan begitu keakurasian hasil klasifikasi dapat meningkat. Dari 120 data siswa yang diuji, terbukti bahwa hasil klasifikasi penerapan teknik unsupervised discretization pada metode naive bayes naik dari 90% menjadi 92.8%.   Abstract Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the controlled field. In previous research, Naive Bayes method has been tested to classify the students department based on the supportive criterias (case study on Madrasah Aliyah PAB 6 Helvetia), and the test result of 100 students data, the classification accuracy is about 90% . in this study, optimizaton is done with a method used earlier by applying Unsupervised Discretization techniques that would transform numerical / continuous criteria into categorical criteria and eliminating one criterion that is considered not affect the accuracy of test results. thus the accuracy of classification results could increase. 120 students data is tested, it is evident that the results of the classification of the application of unsupervised discretization techniques on the Naive Bayes method rose from 90% to 92.8%

Implementation Equal-Width Interval Discretization in Naive Bayes Method for Increasing Accuracy of Students' Majors Prediction

The Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the field of interest. In previous research, Naive Bayes method has been tested to classify the student’s department based on the criteria that support the case study on Private Madrasah Aliyah PAB 6 Helvetia students and the accuracy of the test from 100 student data is 90%. in this study, the researcher developed a previously used method by applying an equal-width interval discretization that would transform numerical or continuous criteria into a categorical criteria with a predetermined k value, different k values ??would be tested to find the best accuracy value. from the 120-student data that have been tested, it is proved that the result of the classification of the application of equal-width interval discretization on the Naive Bayes method with the value of k = 8 is better and increased the accuracy value 91.7% to 93.3%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper.Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform(FRFT) and Shannon entropy. Afterwards, the Welch’s t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10 x K-fold stratified cross validation test showed that this proposed “FRFE +WTT + TSVM” yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed “FRFE +WTT + TSVM” method is superior to 20 state-of-the-art methods

Fully automatic image analysis framework for cervical vertebra in X-ray images

Despite the advancement in imaging technologies, a fifth of the injuries in the cervical spine remain unnoticed in the X-ray radiological exam. About a two-third of the subjects with unnoticed injuries suffer tragic consequences. Based on the success of computer-aided systems in several medical image modalities to enhance clinical interpretation, we have proposed a fully automatic image analysis framework for cervical vertebrae in X-ray images. The framework takes an X-ray image as input and highlights different vertebral features at the output. To the best of our knowledge, this is the first fully automatic system in the literature for the analysis of the cervical vertebrae. The complete framework has been built by cascading specialized modules, each of which addresses a specific computer vision problem. This dissertation explores data-driven supervised machine learning solutions to these problems. Given an input X-ray image, the first module localizes the spinal region. The second module predicts vertebral centers from the spinal region which are then used to generate vertebral image patches. These patches are then passed through machine learning modules that detect vertebral corners, highlight vertebral boundaries, segment vertebral body and predict vertebral shapes. In the process of building the complete framework, we have proposed and compared different solutions to the problems addressed by each of the modules. A novel region-aware dense classification deep neural network has been proposed for the first module to address the spine localization problem. The proposed network outperformed the standard dense classification network and random forestbased methods. Location of the vertebral centers and corners vary based on human interpretation and thus are better represented by probability maps than single points. To learn the mapping between the vertebral image patches and the probability maps, a novel neural network capable of predicting a spatially distributed probabilistic distribution has been proposed. The network achieved expert-level performance in localizing vertebral centers and outperform the Harris corner detector and Hough forest-based methods for corner localization. The proposed network has also shown its capability for detecting vertebral boundaries and produced visually better results than the dense classification network-based boundary detectors. Segmentation of the vertebral body is a crucial part of the proposed framework. A new shapeaware loss function has been proposed for training a segmentation network to encourage prediction of vertebra-like structures. The segmentation performance improved significantly, however, the pixel-wise nature of proposed loss function was not able to constrain the predictions adequately. To solve the problem a novel neural network was proposed which predicts vertebral shapes and trains on a loss function defined in the shape space. The proposed shape predictor network was capable of learning better topological information about the vertebra than the shape-aware segmentation network. The methods proposed in this dissertation have been trained and tested on a challenging dataset of X-ray images collected from medical emergency rooms. The proposed, first-of-its-kind, fully automatic framework produces state-of-the-art results both quantitatively and qualitatively