Australasian Medical Journal
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Reliable epileptic seizure detection using an improved wavelet neural network
Get PDFBackgroundElectroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. AimsThis study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures. Method Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. ResultsThe study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis.ConclusionThe obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process
Using Prediction to Improve Elective Surgery Scheduling
Get PDFBackgroundTimely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. AimsThe aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach.ResultsThe automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80.ConclusionWhile the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques
Spontaneous Tumor Lysis Syndrome in a Case of Multiple Myeloma
Get PDFWe describe a case of a 40-year-old male patient who was found to have multiple myeloma with spontaneous tumour lysis syndrome (TLS), following a compression fracture of the L–2 vertebrae. Multiple myeloma was confirmed by bone marrow analysis and the M–band on serum protein electrophoresis. Hyperuricaemia (26.2 mg/dL), hyperkalaemia (> 7.0 mEq/L), hyperphosphatemia (16.2 mg of phosphorus/dL), normocalcemia and acute kidney injury, prior to anticancer treatment suggested spontaneous TLS. Inciting events for tumour lysis, such as chemotherapy, dehydration and exposure to steroids were absent. Patient received hydration, hypourecemic drugs and haemodialysis. This case report highlights the rare presentation of multiple myeloma with spontaneous TLS
Subcutaneous zygomycosis caused by Mucor hiemalis in an immunocompetent patient
Get PDFZygomycosis is an opportunistic fungal infection with a high mortality rate. It is known to cause invasive disease in immunocompromised hosts but it may produce only cutaneous/ subcutaneous infections in immunocompetent hosts. Treatment is difficult due to its fulminant course and lack of effective anti-fungal drugs. Here, we report a rare case of subcutaneous zygomycosis caused by Mucor hiemalis in an immunocompetent patient without any debilitating illness. The patient was successfully treated by aggressive surgical debridement and anti-fungal therapy
