Deteksi Penyakit Epilepsi Berdasarkan Data EEG Otak Manusia Dengan Menggunakan Independent Component Analysis, Wavelet Transform, Dan Multilayer Perceptron

Epilepsi merupakan salah satu kelainan pada otak manusia yang tidak dapat disembuhkan. Penyakit ini menimbulkan kejang pada tubuh dan sangat mengganggu aktivitas. Pada tingkat yang parah, epilepsi dapat membahayakan nyawa penderitanya. Oleh sebab itu, epilepsi harus dideteksi secara dini agar penderita segera mendapatkan penanganan yang tepat sehingga keadaannya tidak memburuk.. Pada Tugas Akhir ini, deteksi epilepsi dilakukan dengan menggunakan beberapa metode, yaitu Independent Component Analysis, Wavelet Transform, dan Multilayer Perceptron. Hasil deteksi diklasifikasikan ke dalam tiga kelas, yaitu normal, epilesi tidak kejang, dan epilepsi kejang. Data rekaman electroencephalogram (EEG) yang digunakan berasal dari ''Klinik für Epileptologie, Universität Bonn” yang diperoleh secara online. Hasil pendeteksian terbaik dihasilkan dari model yang menggunakan teknik Single Channel Independent Component Analysis pada Independent Component Analysis sebagai viii penghilang derau dan ektraksi fitur Discrete Wavelet Transform Daubechies 6 dengan 4 level. Berdasarkan uji coba, metode tersebut menghasilkan akurasi sebesar 92.09%. ======================================================================================================== Epilepsy is one of disorders in human brain that is cannot be healed. This diesease occurs seizuring which bothers patients’ activities. In the worst condition, it endangers patients’ life. Therefore , the epilepsy must be detected since the early beginning so that patients get a proper treatment immediately for avoiding worse condition. On this undergraduated thesis, epilepsy detection was build by using three methods; The Independent Component Analysis, Wavelet Transform, and Multilayer Perceptron. The result of detection was classified into 3 classes. They were normal, epilepsy non-seizure, and epilepsy seizure. While the electroencephalogram (EEG) record data used was taken from ''Klinik für Epileptologie, Universität Bonn” website. The best result of classification was achieved by a model that was build by Single Channel Independent Component Analysis technique in Independent Component Analysis, Wavelet Transform, and Multilayer Perceptron as noise removal and Discrete Wavelet Transform using Daubechies 6 with 4 level as feature extraction. Based on test result, the method above obtained an acurracy of 92.09%

A reliable neural network-based decision support system for breast cancer prediction

PhD ThesisAxillary lymph node (ALN) metastasis status is an important prognostic marker in breast cancer and is widely employed for tumour staging and defining an adjuvant therapy. In an attempt to avoid invasive procedures which are currently employed for the diagnosis of nodal metastasis, several markers have been identified and tested for the prediction of ALN metastasis status in recent years. However, the nonlinear and complex relationship between these markers and nodal status has inhibited the effectiveness of conventional statistical methods as classification tools for diagnosing metastasis to ALNs. The aim of this study is to propose a reliable artificial neural network (ANN) based decision support system for ALN metastasis status prediction. ANNs have been chosen in this study for their special characteristics including nonlinear modelling, robustness to inter-class variability and having adaptable weights which makes them suitable for data driven analysis without making any prior assumptions about the underlying data distributions. To achieve this aim, the probabilistic neural network (PNN) evaluated with the .632 bootstrap is investigated and proposed as an effective and reliable tool for prediction of ALN metastasis. For this purpose, results are compared with the multilayer perceptron (MLP) neural network and two network evaluation methods: holdout and cross validation (CV). A set of six markers have been identified and analysed in detail for this purpose. These markers include tumour size, oestrogen receptor (ER), progesterone receptor (PR), p53, Ki-67 and age. The outcome of each patient is defined as metastasis or non-metastasis, diagnosed by surgery. This study makes three contributions: firstly it suggests the application of the PNN as a classifier for predicting the ALN metastasis, secondly it proposes a the .632 bootstrap evaluation of the ANN outcome, as a reliable tool for the purpose of ALN status prediction, and thirdly it proposes a novel set of markers for accurately predicting the state of nodal metastasis in breast cancer. Results reveal that PNN provides better sensitivity, specificity and accuracy in most marker combinations compared to MLP. The results of evaluation methods’ comparison demonstrate the high variability and the existence of outliers when using the holdout and 5-fold CV methods. This variability is reduced when using the .632 bootstrap. The best prediction accuracy, obtained by combining ER, p53, Ki-67 and age was 69% while tumour size and p53 were the most significant individual markers. The classification accuracy of this panel of markers emphasises their potential for predicting nodal spread in individual patients. This approach could significantly reduce the need for invasive procedures, and reduce post-operative stress and morbidity. Moreover, it can reduce the time lag between investigation and decision making in patient management.ORS Award Schem

Breast cancer prediction and cross validation using multilayer perceptron neural networks

The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers has prohibited an accurate prognosis. The aim of this paper is to investigate the effectiveness of a multilayer perceptron (MLP) for the aim of predicting breast cancer progression using a set of four biomarkers of breast tumours. A further objective of the study is to explore the predictive potential of these markers in defining the state of nodal involvement in breast cancer. Two methods of outcome evaluation viz. stratified and simple k-fold cross validation (CV) are also studied in order to assess their accuracy and reliability for neural network validation. We used output accuracy, sensitivity and specificity for selecting the best validation technique besides evaluating the network outcome for different combinations of markers. Findings suggest that ANN-based analysis provides an accurate and reliable platform for breast cancer prediction given that an appropriate design and validation method is employed

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place