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Al-Manie, Mohammed A.
- Deconvolution Methods for Biomedical Signals Analysis
Abstract Views :504 |
PDF Views:124
Authors
Affiliations
1 Computer Research Institute, KACST P.O. Box 6080, Riyadh 11442, SA
1 Computer Research Institute, KACST P.O. Box 6080, Riyadh 11442, SA
Source
Indian Journal of Science and Technology, Vol 3, No 2 (2010), Pagination: 105-109Abstract
In this paper, a deconvolution approach based on time frequency representation (TFR) methods is used for the estimation and analysis of biomedical signals. Chosen as examples are electroencephalogram (EEG) as well as the Electrocardiogram (ECG) signals for normal and abnormal patients. In particular, an iterative procedure is applied to calculate the required time-frequency distributions for the different types of cases under study. The deconvolution method can be defined as the process of recovering the input to some system from its output given information about that particular system. This kind of procedure is used in the field of time-frequency analysis for enhancing the resolutions of the signals under testing. These advantages are used in this paper for the biomedical applications area.Keywords
Sleep Scoring, Electroencephalogram, Electrocardiogram, DeconvolutionReferences
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- Hassanpour H, Mesbah M and Boashash B (2004) EEG spike detection using time-frequency analysis. ICASSP. 5, 421-424.
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- Maglaveras N, Stamkopoulos T, Diamantaras K, Pappas C and Strintzis M (1998) ECG pattern recognition and classification using non-linear transformations and neural networks: a review. Int. J. Medical Informatics. 52, 191-208.
- Paul J, Reddy M and Kumar J (1999) A Cepstraltransformation technique for dissociation of QRS-typeECG signals using DCT. Signal Process. 29-39.
- Penzel T and Conradt R (2000) Computer based sleep recording and analysis. Sleep Medicine Rev. 47(2), 131-148.
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- Shimada T and Shiina T (2000) Detection of characteristic waves of sleep EEG by neural network analysis. IEEE Trans. Biomedical Eng. 47(3), 369- 379.
- Sivannarayana N and Reddy D (1999) Biorthogonal wavelet transform for ECG parameters estimation. Medical Eng. Physics. 21, 167-174.
- Arabic Speech Segmentation: Automatic Verses Manual Method and Zero Crossing Measurements
Abstract Views :492 |
PDF Views:119
Authors
Affiliations
1 Computer Research Institute, KACST, P.O. Box 6080, Riyadh 11442, SA
1 Computer Research Institute, KACST, P.O. Box 6080, Riyadh 11442, SA
Source
Indian Journal of Science and Technology, Vol 3, No 12 (2010), Pagination: 1134-1138Abstract
In this paper, a comparison between the automatic and manual approach of speech segmentation for the Arabic speech is conducted. In this approach, the automatic method, using the energy level measurement is compared to the manual segmentation procedure. The traditional zero crossing method commonly used for speech processing is also included in this work. The energy measurement method is based on dividing the uttered tokens into different levels. For instance, the Arabic language phonemes are divided into two energy regions: unvoiced phonemes which can be categorized as low energy include the sounds / س / (/s/) and / ه / (/h/). On the other hand, vowels and semi-vowels such as / َ / ( فتحه ) (/ ‘a /) and / و / (/w/) are labeled as high energy. Voiced fricatives, for instance, the sounds / ز / (/z/) and / /ع (/aa/) are classified as high energy phonemes.Keywords
Manual Speech Segmentation, Automatic Speech Segmentation, Voiced Phonemes, Voiceless PhonemesReferences
- Alghmadi M (2003) KACST Arabic phonetic database. The 15th Int. Congress of Phonetics Sci. 3109-3112.
- Cherif A, Bouafif L and Dabbabi T (2001) Pitch detection and formant analysis of Arabic speech processing. Appl. Acoustics. 1129-1140.
- Demuynck K and Laureys T (2002) A comparison of different approaches to automatic speech segmentation. Lecturer notes in Computer Sci. 2448, 385-406.
- Essa O (2005) Using prosody in automatic segmentation of speech. Computer Science Dept., University of South Carolina.
- Hemert V (1991) Automatic segmentation of speech. IEEE Trans. on Signal Processing. 1008-1012.
- Lee Y, Papineni K, Roukos S, Emam O and Hassan H (2003) Language model based arabic word segmentation. Proc. of the 41st Annual meeting of the Association for Computational Linguistics. 399-406.
- Tolba M, Nazmy T, Abdelhamid A and Gadallah M (2005) A novel method for Arabic consonant/vowel segmentation using wavelet transform. Proc. of IJICIS. 353-364.