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

50 Years of Image Analysis

Computational and Mathematical Methods in Medicine

Artificial intelligence techniques for photovoltaic applications: A review

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

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Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice.

From the Publisher:
Provides comprehensive treatment of the theory of both static and dynamic neural networks.
* Theoretical concepts are illustrated by reference to practical examples Includes end-of-chapter exercises and end-of-chapter exercises.

Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

In recent years, a deep convolutional neural network (DCNN) has attracted great attention due to its outstanding performance in recognition of natural images. However, the DCNN performance for medical image recognition is still uncertain because collecting a large amount of training data is difficult. To solve the problem of the DCNN, we adopt a transfer learning strategy, and demonstrate feasibilities of the DCNN and of the transfer learning strategy for mass detection in mammographic images. We adopt a DCNN architecture that consists of 8 layers with weight, including 5 convolutional layers, and 3 fully-connected layers in this study. We first train the DCNN using about 1.2 million natural images for classification of 1,000 classes. Then, we modify the last fully-connected layer of the DCNN and subsequently train the DCNN using 1,656 regions of interest in mammographic image for two classes classification: mass and normal. The detection test is conducted on 198 mammographic images including 99 mass images and 99 normal images. The experimental results showed that the sensitivity of the mass detection was 89.9 % and the false positive was 19.2 %. These results demonstrated that the DCNN trained by transfer learning strategy has a potential to be a key system for mammographic mass detection computer-aided diagnosis (CAD). In addition, to the best of our knowledge, our study is the first demonstration of the DCNN for mammographic CAD application.

Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis

It is important to know about a sudden blood pressure change that occurs in everyday life and may pose a danger to human health. However, monitoring the blood pressure variation in daily life is difficult because a bulky and expensive sensor is needed to measure the blood pressure continuously. In this study, a new non-contact method is proposed to estimate the blood pressure variation using video images. In this method, the pulse propagation time difference or instantaneous phase difference is calculated between two pulse waves obtained from different parts of a subject's body captured by a video camera. The forehead, left cheek, and right hand are selected as regions to obtain pulse waves. Both the pulse propagation time difference and instantaneous phase difference were calculated from the video images of 20 healthy subjects performing the Valsalva maneuver. These indices are considered to have a negative correlation with the blood pressure variation because they approximate the pulse transit time obtained from a photoplethysmograph. However, the experimental results showed that the correlation coefficients between the blood pressure and the proposed indices were approximately 0.6 for the pulse wave obtained from the right hand. This result is considered to be due to the difference in the transmission depth into the skin between the green and infrared light used as light sources for the video image and conventional photoplethysmogram, respectively. In addition, the difference in the innervation of the face and hand may be related to the results.

Techniques for estimating blood pressure variation using video images

In this chapter, the authors provide fundamental principles of Higher Order Neural Units (HONUs) and Higher Order Neural Networks (HONNs) for modeling and simulation. An essential core of HONNs can be found in higher order weighted combinations or correlations between the input variables and HONU. Except for the high quality of nonlinear approximation of static HONUs, the capability of dynamic HONUs for the modeling of dynamic systems is shown and compared to conventional recurrent neural networks when a practical learning algorithm is used. In addition, the potential of continuous dynamic HONUs to approximate high dynamic order systems is discussed, as adaptable time delays can be implemented. By using some typical examples, this chapter describes how and why higher order combinations or correlations can be effective for modeling of systems. Zeng-Guang Hou The Chinese Academy of Sciences, China

Fundamentals of Higher Order Neural Networks for Modeling and Simulation

Sudden changes in blood pressure are frequently caused by daily events such as exercise, stressful work, and moving between different temperature environments. Sometimes, an abrupt change in blood pressure can endanger human lives. To mitigate such a risk, several studies have previously proposed contactless methods for monitoring changes in blood pressure; however, the number of studies on this issue is insufficient. In previous methods, changes in blood pressure were estimated using the propagation time of the pulse waves obtained from two different skin areas on the basis of an analysis of images from video recordings, which is called a video plethysmogram. However, the relative positional relationship of the two skin areas had to be maintained during the measurement, making the approach restrictive. To solve this problem, in the present study, we propose a new method using the video plethysmogram obtained from only one skin area. In particular, the degree of distortion in the video plethysmogram is calculated as a new index to estimate the blood pressure. On the basis of the results of our experiments with 20 healthy human subjects, we ascertain that the proposed index obtained from the palm area is correlated with the blood-pressure variability as well as the previous approach.

Noriyasu Homma

Papers

Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis

Techniques for estimating blood pressure variation using video images

Fundamentals of Higher Order Neural Networks for Modeling and Simulation

Contactless Technique for Measuring Blood-Pressure Variability from One Region in Video Plethysmography