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

The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.

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A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.

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Face recognition by independent component analysis

An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sou...

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Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources

In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

A wavelet-based ECG delineator: evaluation on standard databases

In order to robustly recognize distorted speech, use of visual information has been proven valuable in many recent investigations. However, visual features may not always be available, and they can be unreliable in unfavorable recording conditions. The same is true for distorted audio information, where noise and interference can corrupt some of the acoustic speech features used for recognition. In this paper, missing feature techniques for coupled HMMs are shown to be successful in coping with both uncertain audio and video information. Since binary uncertainty information may be easily obtained at little computational effort, this results in an effective approach that can be implemented to obtain significant performance improvements for a wide range of statistical model based audiovisual recognition systems.

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Audiovisual speech recognition with missing or unreliable data.

Ein Signalauswerteverfahren zur Detektion von QRS-Komplexen in Elektrokardiogramm-Signalen weist folgende Verfahrensschritte auf: A signal analysis method for detection of QRS complexes in electrocardiogram signals comprises the following method steps: Abtasten des EKG-Signals (4) und Umwandlung in diskrete, zeitlich aufeinanderfolgende Signalwerte (x(n), x fq (n)), Sampling said ECG-signal (4) and conversion into discrete, chronologically successive signal values ​​(x (n), x fq (n)), Vergleichen der Signalwerte (x(n), x fq (n)) mit einer adaptiv aus diesen ermittelten Schwellenwertfunktion (K(n)), Comparing the signal values ​​(x (n), x fq (n)) with an adaptively determined threshold value of this function (K (n)), Feststellen einer Haufigkeitszahl (D(n)) innerhalb eines definierten Segmentes der aufeinanderfolgenden Signalwerte (x(n), x fq (n)), mit der vorzugsweise die Betrage der Signalwerte (x(n), x fq (n)) die Schwellenwertfunktion (K(n)) unterschreiten, und Detecting a frequency number (D (n)) within a defined segment of the successive signal values ​​(x (n), x fq (n)), with preferably the amounts of the signal values ​​(x (n), x fq (n)), the threshold value function (K (n)) below, and Vergleichen der ermittelten Haufigkeitszahl (D(n)) mit einem definierten Grenzwert (Θ), wobei ein Unterschreiten des Grenzwerts (Θ) signifikant fur das Vorliegen eines QRS-Komplexes (5, 6, 7) in dem definierten Segment des EKG-Signals (4) ist. Comparing the determined frequency number (D (n)) with a defined limit value (Θ), wherein a falling short of the limit value (Θ) significant for the existence of a QRS complex (5, 6, 7) in the defined segment of the ECG signal ( 4).

Signal evaluation method for detecting QRS complexes in electrocardiogram signals

Prophylaxis and rehabilitation of cardiovascular disease require the development of biosignal acquisition and processing devices that are capable of supporting patients in their everyday life. This paper presents a Body Sensor Network (BSN) for use in Personal Healthcare applications. It consists of miniaturized sensor modules for electrocardiogram (ECG), photoplethysmogram (PPG) and phonocardiography (PCG) which are wirelessly connected with a coordinator to collect the data. Each sensor module is combined with a tri-axis accelerometer for patient's posture and activity measurement. As it is possible to extract further information about the health state by fusioning data of different biosensors, the wireless link based on IEEE 802.15.4 was extended by a synchronisation mechanism enabling synchronous sampling of the individual sensors. An adaptive application of algorithms for signal pre-processing and analysis allows the reduction of the transferred data.

Wireless Body Sensor Network for low-power motion-tolerant syncronized vital sign measurment

In this paper we demonstrate how uncertainty propagation allows the computation of minimum mean square error (MMSE) estimates in the feature domain for various feature extraction methods using short-time Fourier transform (STFT) domain distortion models. In addition to this, a measure of estimate reliability is also attained which allows either feature re-estimation or the dynamic compensation of automatic speech recognition (ASR) models. The proposed method transforms the posterior distribution associated to a Wiener filter through the feature extraction using the STFT Uncertainty Propagation formulas. It is also shown that non-linear estimators in the STFT domain like the Ephraim-Malah filters can be seen as special cases of a propagation of the Wiener posterior. The method is illustrated by developing two MMSE-Mel-frequency Cepstral Coefficient (MFCC) estimators and combining them with observation uncertainty techniques. We discuss similarities with other MMSE-MFCC estimators and show how the proposed approach outperforms conventional MMSE estimators in the STFT domain on the AURORA4 robust ASR task.

Computing MMSE Estimates and Residual Uncertainty Directly in the Feature Domain of ASR using STFT Domain Speech Distortion Models

We present a new method to tackle the problem of separating mixtures of real sources which have been convolved and time-delayed under real world conditions. To this end, we learn two sets of parameters to unmix the mixtures and to estimate the true density function. The solutions are discussed for feedback and feedforward architectures. Since the quality of separation depends on the modeling of the underlying density we propose different methods to closer approximate the density function using some contest. The proposed density estimation achieves separation of a wider class of sources. Furthermore, we employ the FIR polynomial matrix techniques in the frequency domain to invert a true-phase mixing system. The significance of the new method is demonstrated with the successful separation of two speakers and separation of music and speech recorded with two microphones in a reverberating room.

Reinhold Orglmeister

Papers

Audiovisual speech recognition with missing or unreliable data.

Signal evaluation method for detecting QRS complexes in electrocardiogram signals

Wireless Body Sensor Network for low-power motion-tolerant syncronized vital sign measurment

Computing MMSE Estimates and Residual Uncertainty Directly in the Feature Domain of ASR using STFT Domain Speech Distortion Models

A contextual blind separation of delayed and convolved sources