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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

Brain-computer interfaces for communication and control.

Optical coherence tomography (OCT) has developed rapidly since its first realisation in medicine and is currently an emerging technology in the diagnosis of skin disease. OCT is an interferometric technique that detects reflected and backscattered light from tissue and is often described as the optical analogue to ultrasound. The inherent safety of the technology allows for in vivo use of OCT in patients. The main strength of OCT is the depth resolution. In dermatology, most OCT research has turned on non-melanoma skin cancer (NMSC) and non-invasive monitoring of morphological changes in a number of skin diseases based on pattern recognition, and studies have found good agreement between OCT images and histopathological architecture. OCT has shown high accuracy in distinguishing lesions from normal skin, which is of great importance in identifying tumour borders or residual neoplastic tissue after therapy. The OCT images provide an advantageous combination of resolution and penetration depth, but specific studies of diagnostic sensitivity and specificity in dermatology are sparse. In order to improve OCT image quality and expand the potential of OCT, technical developments are necessary. It is suggested that the technology will be of particular interest to the routine follow-up of patients undergoing non-invasive therapy of malignant or premalignant keratinocyte tumours. It is speculated that the continued technological development can propel the method to a greater level of dermatological use.

https://link.springer.com/content/pdf/bfm%3A978-3-319-06419-2%2F1.pdf

Optical Coherence Tomography

In this paper, we present a simulation study of the effects of Sotalol, a known anti-arrhythmic drug, on the rapid delayed rectifier potassium current (I Kr ). The current is encoded by the Human Ether-a-go-go Related Gene (HERG), which plays a major role in repolarization in mammalian ventricles. HERG is also the target of class III anti-arrhythmic drugs, such as Sotalol. Due to its unique structure and electrophysiological qualities, non-cardiac drugs readily bind with residues inside HERG's intracellular cavity. A novel Markov model was developed to model Sotalol's interaction with HERG. The model was validated using experimental data from HERG expressed in Human Embryonic Kidney (HEK) cells and integrated into the ten Tusscher (2006) human ventricular cell model. The simulation results show that an increase in Sotalol concentration decreases the overall conductance of I Kr over time, resulting in prolongation of the action potential duration. This effect is larger in mid-myocardial than in endocardial and epicardial cells. Therefore, Sotalol-induced effects on cardiac repolarization may result in enhanced transmural dispersion of repolarization in the ventricles, and also in changes in the T wave.

Modelling effects of Sotalol on Action Potential morphology using a novel Markov model of the HERG channel

The smoothed pseudo Wigner-Ville distribution (SPWVD) is used for the time-frequency analysis of variations in RR interval. A novel technique to determine the smoothing window lengths is implemented, and a new heart rate variability (HRV) metric is developed, instantaneous center frequency variability (ICFV), which uses the time-frequency map generated by the SPWVD. The technique is then applied to 50 patients with unexplained falls and age > 60, undergoing head-upright tilt table testing (HUT). Eighteen of the patients were diagnosed with vasovagal syndrome. Attempts at syncope prediction using the new metric is an improvement on traditional techniques: an ICFV less than 0.07 Hz from 90 s to 180 s after tilt is predictive of a negative test (negative predictive value: 0.77). The comorbidity and autonomic degeneration present in elderly patients are thought to be responsible for lowering the negative predictive value.

Time-frequency analysis of the ECG in the diagnosis of vasovagal syndrome in older people

Is Pregnancy Outcome Worse in Type 2 Than in Type 1 Diabetic Women

Mobile health monitoring in the management of long term conditions has potential benefits for patient care, especially when coupled with active adjustment of medication dosage. We report studies of patient-led self-titration of oral glucose lowering medication (OGLM) and insulin in type 2 diabetes, and dose adjustments (including dose increases) in oral chemotherapy for metastatic colorectal or breast cancer. Monitoring compliance was high in each case, and the feasibility of patients self-titrating OGLM or insulin following an agreed treatment plan was demonstrated. Chemotherapy dose increases supported by detailed toxicity profiles collected by phone have also been demonstrated.

Mobile health for drug dose optimisation

The rise of chronic disease and failure in the implementation of adequate prevention strategies places a heavy burden on the health systems of low- and middle-income countries. Despite vast interest in mobile health (mHealth) technologies, there is a lack of evidence for the clinical impact and scalability of mHealth tools for managing chronic diseases in a resource-constrained setting. This paper outlines the development and field evaluation of an mHealth solution in the form of a clinical decision support (CDS) tool. The CDS tool was tailored for use by healthcare providers within a primary care setup in rural India to perform screening and management of cardiovascular disease (CVD) risk. The CDS tool was designed prior to, and during an agile development phase that comprehensively engaged end-users namely primary health centre (PHC) physicians and rural non-physician healthcare workers (NPHWs). Lessons learnt from a pilot implementation are presented to help inform strategies for large-scale evaluation of mHealth technology in resource-constrained settings.

Lionel Tarassenko

Papers

Modelling effects of Sotalol on Action Potential morphology using a novel Markov model of the HERG channel

Time-frequency analysis of the ECG in the diagnosis of vasovagal syndrome in older people

Is Pregnancy Outcome Worse in Type 2 Than in Type 1 Diabetic Women

Mobile health for drug dose optimisation

Lessons from the evaluation of a clinical decision support tool for cardiovascular disease risk management in rural India