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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

Jet engines are remarkable mechanical engineering feats and include more than 100 000 parts. A rudimentary understanding of mechanical engineering concepts helped physicians such as William Harvey, who described the heart as a pump with 1-way valves, make fundamental advances in medicine in the 17th century. Now, well into the 21st century, jet engines are monitored far more intensively than people. This Viewpoint explores how contemporary digital technologies, such as on-engine monitors or wearable sensors and artificial intelligence (AI), can be used to maintain the wellbeing of jet engines and humans. Jet engines are built to very tight specifications and operate within well-defined bounds of normality. In contrast, with the human body, it is still not known what “normal” is for common metrics, such as blood glucose after eating or blood pressure throughout the day and night.1 With jet engines, performance parameters (eg, temperatures, pressures, and shaft speeds) and vibration data are continuously monitored in flight to assess engine health. Advanced signal processing and machine learning are applied to the engine data to provide early warnings of incipient problems that would require unscheduled engine maintenance and take the plane out of service. End-of-flight summary reports that distill gigabytes of data are transmitted to the maintenance engineers on the ground.

Monitoring Jet Engines and the Health of People.

Much has been written regarding the merits and demerits of analog and digital techniques for the integration of neural networks. Proponents of both approaches are often scathing about alternatives, and blind to the shortcomings of their own work. In this chapter, we will attempt to give an honest account of a body of work that builds VLSI neural circuitry using digital voltage and current pulses to carry information and perform computation, in a manner similar to the biological nervous system. Although this parallel did not, and does not, form the motivation for the use of this technique, it is an interesting aspect of the work, and was in part its inspiration.

Pulsed silicon neural networks - following the biological leader -

© 2020 The Authors. Health Expectations published by John Wiley & Sons Ltd Background: Having a stroke or transient ischaemic attack increases the risk of a subsequent one, especially with high blood pressure (BP). Home-based BP management can be effective at maintaining optimal BP. Objective: To describe the optimization of a digital intervention for stroke patients and the value of participant diversity, using the person-based approach (PBA) and integral patient and public involvement (PPI). Setting and participants: Stroke patients recruited from primary care and community settings, and health-care professionals in primary care, in England and Ireland. Design: Three linked qualitative studies conducted iteratively to develop an intervention using the PBA, with integral PPI. Intervention: The BP: Together intervention, adapted from existing BP self-monitoring interventions, is delivered via mobile phone or web interface to support self-monitoring of BP at home. It alerts patients and their clinicians when a change in antihypertensive medication is needed. Findings: Feedback from a diverse range of participants identified potential barriers, which were addressed to improve the intervention accessibility, feasibility and persuasiveness. Easy-to-read materials were developed to improve usability for patients with aphasia and lower literacy. The importance of including family members who support patient care was also highlighted. Feedback messages regarding medication change were refined to ensure usefulness for patients and clinicians. Discussion: Input from PPI alongside qualitative research with a diverse study sample allowed the creation of a simple and equitable BP management intervention for stroke patients. Patient involvement: Two PPI co-investigators contributed to design, conduct of study, data interpretation and manuscript preparation; community PPI sessions informed early planning. Study participants were stroke patients and family members.

Optimizing a digital intervention for managing blood pressure in stroke patients using a diverse sample: Integrating the person‐based approach and patient and public involvement

Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors onwards to monitoring patients in their own homes. The problem is still very challenging, particularly during the motion for large segments of data, where results from different methods often do not agree. The authors aim to propose a new technique which performs motion reduction from PPG signals with the help of simultaneous acceleration signals where the PPG and accelerometer sensors need to be embedded in the same sensor unit. This method also reconstructs motion corrupted PPG signals in the Hilbert domain. An auto-regressive (AR) based technique has been used to estimate the RR from reconstructed PPGs. The proposed method has provided promising results for the estimation of RRs and their variations from PPG signals corrupted with motion artefact. The proposed platform is able to contribute to continuous in-hospital and home-based monitoring of patients using PPG signals under various conditions such as rest and motion states.

Lionel Tarassenko

Papers

Monitoring Jet Engines and the Health of People.

Pulsed silicon neural networks - following the biological leader -

Optimizing a digital intervention for managing blood pressure in stroke patients using a diverse sample: Integrating the person‐based approach and patient and public involvement

Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry.

Innovations in Pulse Stream Neural VLSI : Arithmetic and Communications