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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

Novelty detection, or one-class classification, aims to determine if data are "normal" with respect to some model of normality constructed using examples of normal system behaviour. If that model is composed of generative probability distributions, the extent of "normality" in the data space can be described using Extreme Value Theory (EVT), a branch of statistics concerned with describing the tails of distributions. This paper demonstrates that existing approaches to the use of EVT for novelty detection are appropriate only for univariate, unimodal problems. We generalise the use of EVT for novelty detection to the analysis of data with multivariate, multimodal distributions, allowing a principled approach to the analysis of high-dimensional data to be taken. Examples are provided using vital-sign data obtained from a large clinical study of patients in a high-dependency hospital ward.

/pdf/novelty-detection-with-multivariate-extreme-value-statistics-1a2xr2ho9c.pdf

Novelty Detection with Multivariate Extreme Value Statistics

Results from working analog VLSI implementations of two different pulse stream neural network forms are reported. The circuits are rendered relatively invariant to processing variations, and the problem of cascadability of synapses to form large systems is addressed. A strategy for interchip communication of large numbers of neural states has been implemented in silicon and results are presented. The circuits demonstrated confront many of the issues that blight massively parallel analog systems, and offer solutions. >

Integrated pulse stream neural networks: results, issues, and pointers

We conducted a randomised controlled trial of mandated five-channel physiological monitoring vs standard care, in acute medical and surgical wards in a single UK teaching hospital. In all, 402 high-risk medical and surgical patients were studied. The primary outcome was the proportion of patients experiencing one or more major adverse events, including urgent staff calls, changes to higher care levels, cardiac arrests or death, in 96 h following randomisation. Secondary outcomes were the proportion of patients requiring acute treatment changes, and the 30-day and hospital mortality. In the 96 h following randomisation, 113 (56%) patients in the monitored arm and 116 (58%) in the control arm (OR 0.94, 95% CI 0.63-1.40, p = 0.76) had a major event. An acute change in treatment was necessary in 107 (53%) monitored patients and 101 (50%) control patients (OR 0.55, 95% CI 0.87-1.29). Thirty-four (17%) monitored patients and 35 (17%) control patients died within 30 days. Thirteen patients in the control group received full five-channel monitoring at the request of the ward staff. We conclude that mandated electronic vital signs monitoring in high risk medical and surgical patients has no effect on adverse events or mortality.

https://associationofanaesthetists-publications.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2044.2006.04818.x

A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients.

Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into “intelligent” analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner. This paper has two aims: 1) proposal of a novel, patient-personalized system for analysis and inference in the presence of data uncertainty, typically caused by sensor artifact and data incompleteness; 2) demonstration of the method using a large-scale clinical study in which 200 patients have been monitored using the proposed system. This latter provides much-needed evidence that personalized e-health monitoring is feasible within an actual clinical environment, at scale, and that the method is capable of improving patient outcomes via personalized healthcare.

https://www.robots.ox.ac.uk/~davidc/pubs/tbme2013_gp.pdf

Gaussian Processes for Personalized e-Health Monitoring With Wearable Sensors

Background: Telehealth shows promise for supporting patients in managing their long-term health conditions, such as chronic obstructive pulmonary disease (COPD). However, it is currently unclear how patients, and particularly older people, may benefit from these technological interventions. Aim: To explore patients' expectations and experiences of using a mobile telehealth-based (mHealth) application and to determine how such a system may impact on their perceived wellbeing and ability to manage their COPD. Design and setting: Embedded qualitative study using interviews with patients with COPD from various community NHS services: respiratory community nursing service, general practice, and pulmonary rehabilitation. Method: An embedded qualitative study was conducted to which patients were recruited using purposive sampling to achieve maximum variation. Interviews were carried out prior to receiving the mHealth system and again after a 6-month period. Data were analysed using a grounded theory approach. Results: The sample comprised 19 patients (aged 50-85 years) with varied levels of computer skills. Patients identified no difficulties in using the mHealth application. The main themes encapsulating patients' experience of using the mHealth application related to an increased awareness of the variability of their symptoms (onset of exacerbation and recovery time) and reassurance through monitoring (continuity of care). Conclusion: Patients were able to use the mHealth application, interpret clinical data, and use these within their self-management approach regardless of previous knowledge. Telehealth interventions can complement current clinical care pathways to support self-management behaviour. ©British Journal of General Practice.

Lionel Tarassenko

Papers

Novelty Detection with Multivariate Extreme Value Statistics

Integrated pulse stream neural networks: results, issues, and pointers

A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients.

Gaussian Processes for Personalized e-Health Monitoring With Wearable Sensors

Using a mobile health application to support self-management in COPD: a qualitative study