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A fast algorithm for automated independent process separation from single channel biomedical signal recordings: FastIPA

A fast algorithm for automated independent process separatio...

引用

4th IET International Conference on Advances in Medical, signal and Information processing, MEDSIP 2008

作者： James, C.J. Davies, M.E. Signal Processing and Control Group ISVR University of Southampton United Kingdom IDCOM Joint Research Institute for Signal and Image Processing University of Edinburgh United Kingdom

ISBN: (纸本)9780863419348

Independent component analysis (ICA) has found many uses in source separation in biomedical signals. We highlight a methodology and put forward an algorithm which allows single channel ICA to be performed on single channel biomedical signal recordings. The algorithm uses a fast, deflationary approach to efficiently extract independent processes underlying the single channel recordings. We show that for processes which are reasonably spectrally disjoint the algorithm can separate out individual sources. We show examples of this using brain signal recordings and abdominal foetal recordings.

关键词： Independent component analysis

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Contrasting spatial, temporal and Spatio-Temporal ICA applied to ictal EEG recordings

Contrasting spatial, temporal and Spatio-Temporal ICA applie...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Christopher J. James Mike E. Davies Signal Processing and Control Group ISVR University of Southampton UK IDCOM and the Joint Research Institute for Signal and Image Processing University of Edinburgh UK

In this paper we contrast three implementations of Independent Component Analysis (ICA) as applied to epileptic scalp electroencephalographic (EEG) recordings, these are; Spatial (Ensemble) ICA, Temporal (single-channel) ICA and Spatio-Temporal ICA. These techniques are based on information derived from both multi-channel as well as single channel biomedical signal recordings. We assess the suitability of the three techniques in isolating and extracting out epileptic seizure sources. Although our results are preliminary in nature, we show that standard implementations of ICA (ensemble ICA) are lacking when attempting to extract complex underlying activity such as ictal activity in the EEG. Temporal ICA performs well in separating underlying sources, although it is clearly lacking in spatial information. Spatio-Temporal ICA has the advantage of using temporal information to inform the ICA process, aided by the spatial information inherent in multi-channel recordings. This work is being expanded for seizure onset analysis through scalp EEG.

关键词： Integrated circuits Information filters Electroencephalography Delays Independent component analysis

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A fast algorithm for automated independent process separation from single channel biomedical signal recordings: FastIPA

A fast algorithm for automated independent process separatio...

引用

4th IET International Conference on Advances in Medical, signal and Information processing - MEDSIP 2008

作者： C.J. James M.E. Davies Signal Processing and Control Group ISVR University of Southampton UK IDCOM and the Joint Research Institute for Signal and Image Processing University of Edinburgh UK

关键词： Independent component analysis (ICA) Single channel ICA Blind source separation Automated source extraction Independent process analysis Blind source separation Recordings single-channel algorithms signals Independent component analysis A* Algorithm Source separation Automation islet cell antibody

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Geometrical feature extraction for robust speech recognition

Geometrical feature extraction for robust speech recognition

引用

39th Asilomar Conference on signals, Systems and Computers

作者： Xiaokun, Li Kwan, Chiman Signal/Image Processing and Control Group Intelligent Automation Inc. Rockville MD 20855 United States

ISBN: (纸本)1424401313

Visual information from lip contour has been successfully shown to improve the robustness of automatic speech recognition especially in noisy environments. In this paper, a novel method for lip reading is presented. In the method, hue information of input images is used for lip area detection. Then, a set of morphological operations is applied to detect lip contour. Polynomial fitting is designed for geometrical feature extraction. With the extracted features, Hidden Markov Models and Gaussian Mixture Models are trained to recognize speech. The experimental results demonstrated that the proposed method improved speech recognition rates in noisy environment. Another advantage of the method is its robustness to lighting variances. © 2005 IEEE.

关键词： Speech recognition

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Geometrical Feature Extraction for Robust Speech Recognition

Geometrical Feature Extraction for Robust Speech Recognition

引用

Asilomar Conference on signals, Systems & Computers

作者： Xiaokun Li Chiman Kwan Signal/Image Processing and Control Group Intelligent Automation Inc. Rockville MD USA

Visual information from lip contour has been successfully shown to improve the robustness of automatic speech recognition especially in noisy environments. In this paper, a novel method for lip reading is presented. In the method, hue information of input images is used for lip area detection. Then, a set of morphological operations is applied to detect lip contour. Polynomial fitting is designed for geometrical feature extraction. With the extracted features, hidden Markov models and Gaussian mixture models are trained to recognize speech. The experimental results demonstrated that the proposed method improved speech recognition rates in noisy environment. Another advantage of the method is its robustness to lighting variances

关键词： Feature extraction Robustness Speech recognition Hidden Markov models Discrete cosine transforms Mouth Automatic speech recognition Shape Working environment noise Morphological operations

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controlling the false positive detection rate in fuzzy clustering of fMRI data

Controlling the false positive detection rate in fuzzy clust...

引用

IEEE International Symposium on Biomedical Imaging

作者： H. Jahanian H. Soltanian-Zadeh G.A. Hossein-Zadeh Signal and Image Processing Group School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Elec. Eng. Department Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. controlling the false positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this paper, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space.

关键词： Fuzzy control Clustering methods Hemodynamics Clustering algorithms Brain signal processing image processing Intelligent systems Process control control systems

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