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

Data Mining Practical Machine Learning Tools and Techniques

Matrix Factorization Techniques for Recommender Systems

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

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Deep learning for healthcare: review, opportunities and challenges.

Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.

Body Area Networks: A Survey

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

Vehicle area networks form the backbone of future intelligent transportation systems.

Progress and challenges in intelligent vehicle area networks

This paper presents a new test-data compression technique that uses exactly nine codewords. Our technique aims at precomputed data of intellectual property cores in system-on-chips and does not require any structural information of cores. The technique is flexible in utilizing both fixed- and variable-length blocks. In spite of its simplicity, it provides significant reduction in test-data volume and test-application time. The decompression logic is very small and can be implemented fully independent of the precomputed test-data set. Our technique is flexible and can be efficiently adopted for single- or multiple-scan chain designs. Experimental results for ISCAS'89 benchmarks illustrate the flexibility and efficiency of the proposed technique.

Nine-coded compression technique for testing embedded cores in SoCs

This article mixes two encoding techniques to reduce test data volume, test pattern delivery time, and power dissipation in scan test applications. This is achieved by using run-length encoding followed by Huffman encoding. This combination is especially effective when the percentage of don't cares in a test set is high, which is a common case in today's large systems-on-chips (SoCs). Our analysis and experimental results confirm that achieving up to an 89% compression ratio and a 93% scan-in power reduction is possible for scan-testable circuits such as ISCAS89 benchmarks.

RL-huffman encoding for test compression and power reduction in scan applications

Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient's physician. Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist-worn device and which in combination produce a very low false positive rate. Stage II looks for a specific pattern created by these three biosignals. For the patients whose seizures cannot be detected by Stage II, Stage III detects seizures using limited-channel electroencephalogram (EEG) monitoring with at most three electrodes. Out of 10 patients, Stage I recognized all 11 seizures from seven patients, Stage II detected all 10 seizures from six patients and Stage III detected all of the seizures of two out of the three patients it analyzed.

Mehrdad Nourani

Papers

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

Progress and challenges in intelligent vehicle area networks

Nine-coded compression technique for testing embedded cores in SoCs

RL-huffman encoding for test compression and power reduction in scan applications

Multi-Biosignal Analysis for Epileptic Seizure Monitoring.