1:00, ITT-P2.1
A NEW FALSE POSITIVE REDUCTION METHOD FOR MCCS DETECTION IN DIGITAL MAMMOGRAPHY
L. ZHANG, W. QIAN, R. SANKAR, D. SONG, R. CLARK
A new mixed feature multistage false positive (FP) reduction method has been developed for improving the FP reduction performance. Eleven features were extracted from both spatial and morphology domains in order to describe the micro-calcification clusters (MCCs) from different perspectives. These features are grouped into three categories: gray-level description, shape description and clusters description. Two feature sets that focus on describing MCCs on every single calcification and on clustered calcifications, respectively, were combined with a back-propagation (BP) neural network with Kalman filter (KF) to obtain the best performance of FP reduction. First, 9 of the 11 gray-level description and shape description features were employed with BP neural network to eliminate all the obvious FP calcifications in the image. Second, the remaining MCCs will be classified into several clusters by a widely used criterion in clinical practice, then the two cluster description features will be added to the first feature set to eliminate the FP clusters from the remaining MCCs. The performance results of this approach was obtained using an image database of 100 real cases of patient's mammogram images in H. Lee Moffitt Cancer Center imaging program.

1:00, ITT-P2.2
WAVELET ANALYSIS OF THE TIMING OF INDIVIDUAL COMPONENTS OF THE ECG SIGNAL
M. BURKE, M. NASOR
ECG recordings were obtained from 21 healthy subjects aged between 13 and 65 years, over a range of heart rate extending from 46 to 184 beats/min (bpm). A wavelet transform method, based on the Mexican Hat wavelet was then used to precisely locate the positions of the onset, peak and termination of individual components in the ECG signal. These times were then classified according to the heart rate associated with the cardiac cycle to which the component belonged. Second order equations in the square root of the cardiac cycle time,T(R-R),were fitted to the data obtained for each component to characterize its timing variation.

1:00, ITT-P2.3
NEWBORN EEG SEIZURE PATTERN CHARACTERISATION USING TIME-FREQUENCY ANALYSIS
B. BOASHASH, M. MESBAH, P. COLDITZ
Previous techniques for seizure detection in newborn are inefficient. The main reason for their relative poor performance resides in their assumption of stationarity of the EEG. To remedy this problem, we use time-frequency distributions (TFD) to analyse and characterise the newborn EEG seizure patterns as a first step toward a time-frequency (TF) based seizure detection and classification scheme. This paper presents the results of the analysis of these time-frequency patterns for two abnormal newborn EEGs. We demonstrate that the newborn EEG seizures are well described by a class of mono- and multi-component linear FM signals. This result is novel and contradicts the simplistic assumptions routinely made in the field.

1:00, ITT-P2.4
SEQUENTIAL METHODS FOR DNA SEQUENCING
N. HAAN, S. GODSILL
Methods for determining the letters of our genetic code, known as DNA sequencing, currently depend on clever use of electrophoresis to generate data sets indicative of the underlying sequence. Typically the subsequent off-line data processing is carried out less intelligently using a combination of heuristic methods. In this paper, we present a new robust model which is able to accurately predict the effect of the many biological processes which are involved, and moreover, which is usable on-line. Off-line methods have been hampered by the need for processing in as little time as possible after the data is generated; performing the processing on-line has enabled a more advanced algorithm to be used with associated improved performance. The algorithm is framed within a Bayesian probabilistic framework, and relies on Sequential Monte Carlo Methods for the model selection and non-linear filtering operations.

1:00, ITT-P2.5
DETECTION AND ESTIMATION OF EMBOLIC DOPPLER SIGNALS USING DISCRETE WAVELET TRANSFORM
N. AYDIN, H. MARKUS, F. MARVASTI
Almost any system for the detection of asymptomatic circulating emboli by Doppler ultrasound employs the fast Fourier Transform (FFT). However, the FFT is not ideally suited to study short-lived embolic signals. The wavelet transform (WT) is an optimized way of analyzing short-lived signals and performs better than the FFT in some respect. We propose a detection method based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals. We used 2 independent data sets, comprising 100 low intensity embolic signals, 100 various type of artifacts and 100 Doppler speckle. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. 98 out of 100 embolic signals were detected as embolic signals for the first data set. 95 out of 100 embolic signals were detected for the second data set when the same threshold values were used.

1:00, ITT-P2.6
A NEW ALGORITHM FOR EEG FEATURE SELECTION USING MUTUAL INFORMATION
M. DERICHE, A. AL-ANI
An EEG feature selection technique for the purpose of classification is developed. The technique selects those features that have maximum mutual information with the specified classes of interest (two classes in this case). Obviously, the simplest way is to consider all possible feature subsets (M out of N). However, even with a small number of features, this procedure is \textit{computationally impossible} and can not be used in practice. Given the fact that most features used to represent EEG signal are \textit{sets} of features (such as AR parameters), our technique considers a trade off between computational cost and chosen feature combination. This contrasts other techniques which select features individually. The classification accuracy of features obtained by applying our technique outperforms those obtained by applying individual feature selection methods when applied to EEG signals.

1:00, ITT-P2.7
ON-LINE EEG CLASSIFICATION AND SLEEP SPINDLES DETECTION USING AN ADAPTIVE RECURSIVE BANDPASS FILTER
R. GHARIEB, A. CICHOCKI
This paper presents a novel adaptive filtering approach for the classification and tracking of the electroencephalogram (EEG) waves. In this approach, an adaptive recursive bandpass filter is employed for estimating and tracking the center frequency associated with each EEG wave. The main advantage inherent in the approach is that the employed adaptive filter only requires one coefficient to be updated. This coefficient represents an efficient distinct feature for each EEG specific wave and its time function reflects the nonstationarity of the EEG signal. Extensive simulations for synthetic and real world EEG data for the detection of sleep spindles show the effectiveness and usefulness of the presented approach.

1:00, ITT-P2.8
DSP IN GENOMICS: PROCESSING AND FREQUENCY-DOMAIN ANALYSIS OF CHARACTER STRINGS
D. ANASTASSIOU
We demonstrate that digital signal processing of biomolecular sequences provides powerful approaches for solving highly relevant problems in bioinformatics by properly mapping character strings into numerical sequences. As examples, we show that color spectrograms visually provide, in the form of local texture, significant information about biomolecular sequences, thus facilitating understanding of local nature, structure and function; we provide an optimization procedure predicting protein-coding regions in DNA sequences including reading frame and coding direction, using both the magnitude and the phase of properly defined Fourier transforms; and we present a digital filtering approach to the process of translating nucleic acids into proteins. These approaches result in alternative mathematical formulations and often provide improved computational techniques for the solution of useful problems in genomic information science and technology. For details please see technical report at www.ee.columbia.edu/cgi-ee-bin/show_archive.pl