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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

Hemodialysis patients have multiple risk factors for small vessel cerebrovascular disease and cognitive dysfunction. Hemodialysis itself may cause clinically significant neurological injury through repetitive cerebral ischemia. However, supporting evidence to date consists of epidemiological associations, expert opinion, and small, single-centre studies of variable methodological quality. Isolating the impact of intra-dialytic hemodynamic instability from underlying renal and vascular disease on clinically relevant functional outcomes would require very large, controlled studies, given the heterogeneity and confounding comorbidities of the population, and the complex relationship between blood pressure and cerebral oxygen delivery. There has been an increase in complementary physiological studies looking directly at intra-dialytic cerebral oxygen balance, which have provided supporting evidence for the occurrence of cerebral ischemia, often independently of hemodynamics. Data suggesting a relationship between these measures of oxygen balance and functional outcomes is only hypothesis-generating at this stage. We advocate the testing of interventions that aim to reduce intra-dialytic cerebral hypoxia (rather than hypotension) in sufficiently powered studies, followed by correlation with validated, longitudinal assessment of clinically relevant neurological damage.

/pdf/cerebral-ischemia-during-hemodialysis-finding-the-signal-in-1p9fhi7nz3.pdf

Cerebral ischemia during hemodialysis-finding the signal in the noise.

The intensive care unit (ICU) admits the most severely ill patients, and the goal of the unit can be interpreted as stabilizing patient physiology. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff. Early detection of physiological deterioration has been highlighted as a key step to reduce ICU readmission and improve patient outcomes. Vital signs were collected for a dataset of 98 patients admitted to an ICU and who survived to hospital discharge after their stay on a step-down ward. A model of physiological normality was developed using data from the day of hospital discharge, and patients were retrospectively evaluated throughout their stay using this model. We show that the physiology of patients being cared for in the ICU improves very rapidly in the three days prior to discharge, and furthermore, that this recovery continues during their stay on the ward, albeit at a slower rate.

Physiological trajectory of patients pre and post ICU discharge 1

This paper describes the use of neural networks to analyse the EEG from patients with recurrent micro-arousal episodes. A bank of bandpass filters and AR modelling have been used separately to represent EEG data from 7 chronic patients, divided into three different classes, each class corresponded to a different a sleep stage. A radial basis function network has been trained to classify normal sleep EEG data from a 2-class set and a 3-class set for automatic micro-arousal scoring of test data. Wakefulness and deep-sleep form the 2-class set, while light/dreaming sleep is included in the 3-class set. The results are compared with the visual scores assigned by an expert. The high percentage (88%-100%) of matches between the automatic and the visual scores demonstrates the ability of neural networks to recognise large and well-defined micro-arousals.

The study of micro-arousals using neural network analysis of the EEG

Awareness is a rare but important complication of general anaesthesia. In its worst manifestation the patient is completely paralysed yet fully conscious and suffering the pain of the operative procedure. The sequelae from such an experience may be significant and lifelong. The paper describes a method, based on parametric modelling and statistical pattern recognition techniques, including neural networks, whereby awareness during general anaesthesia may be detected when present. Two systems are described, the first based solely on the use of the bispectrum, while the second makes use of both spectral and bispectral features. An evaluation on independent test sets shows that both systems have an average accuracy of > 80%, but the variation across individuals is less using the spectral–bispectral system (standard deviation of 16.4% compared with 20.5%). The spectral–bispectral system operates in near real time, requiring only 5 s of data to produce a new estimate of awareness. These estimates are obtained from the output of a trained neural network, which has as its input a set of features extracted from a single channel of electroencephalogram (EEG). The pre-processing of the data prior to input into the neural network is a critical component of the work, and it is here that parametric models have been extensively utilised. The spectral features are extracted from the EEG using a 1 s segment and a lattice filter as the primary model, while the bispectral features are extracted using a 5 s segment and a transversal filter as the underlying model.

Lionel Tarassenko

Papers

Cerebral ischemia during hemodialysis-finding the signal in the noise.

Physiological trajectory of patients pre and post ICU discharge 1

The study of micro-arousals using neural network analysis of the EEG

Use of parametric modelling and statistical pattern recognition in detection of awareness during general anaesthesia

Circadian Blood Pressure Variations Computed From 1.7 Million Measurements in an Acute Hospital Setting.