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Signal classification using wavelets
IEE Seminar on Time-Scale and Time-Frequency Analysis and Applications, 2000There has been an explosion of interest in the study and application of wavelets during the last decade. In this paper we consider the wavelet transform of a square integrable function f(x). This representation of f(x) can be depicted visually by a plot where the x-axis is the translate v, the y-axis the resolution level j, and the z-axis the amplitude
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Myoelectric Signal Classification Using Neural Networks
Biomedizinische Technik/Biomedical Engineering, 1998A feed-forward neural network is used for diagnosis of spastic paralysis. It is a two-layer perceptron and it is able to classify two kinds of myoelectric signal recorded in surface electromyography: the normal EMG and the EMG in the case of spastic paralysis. The myoelectric signal was recorded with a surface electrode pair and sampled at 10 kHz.
M, Ungureanu, R, Strungaru, V, Lazarescu
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Fast Classification Networks For Signal Processing
Circuits, Systems, and Signal Processing, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tang, Kun Won, Kak, Subhash
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Classification of non-stationary neural signals
Journal of Neuroscience Methods, 1998Although a number of methods have been proposed for classification of individual action potentials embedded in multi-unit activity, they have been challenged by non-stationarity. The waveform shapes of action potentials can change rapidly over time as a result of shifts in membrane conductances during extended burst firing sequences and more slowly ...
R K, Snider, A B, Bonds
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Snoring sound classification from respiratory signal
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016Snoring is common in the general population and the irregularity could lead to the presence of Obstructive sleep apnea. Diagnosis of OSA could therefore be made by snoring sound analysis. However, there is still a shortage of robust methods to automatically detect snoring sounds without the need to calibrate for every individual. In this paper, a novel
Mehrnaz, Shokrollahi +4 more
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Multidimensional signal processing for ultrasonic signal classification
AIP Conference Proceedings, 2001Neural network based signal classification systems are being used increasingly in the analysis of large volumes of data obtained in NDE applications. One example is in the interpretation on ultrasonic signals obtained from inspection of welds where signals can be due to porosity, slag, lack of fusion and cracks in the weld region.
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Active Learning Methods for Electrocardiographic Signal Classification
IEEE Transactions on Information Technology in Biomedicine, 2010In this paper, we present three active learning strategies for the classification of electrocardiographic (ECG) signals. Starting from a small and suboptimal training set, these learning strategies select additional beat samples from a large set of unlabeled data. These samples are labeled manually, and then added to the training set.
Pasolli, Edoardo, Melgani, Farid
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Neural Signal Classification Circuits
2016Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop, brain–machine interface. In this chapter, we propose an easily scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator spike ...
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Histones Classification Based on EGFET Signals
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Dysregulation of histones has been implicated in several medical conditions, including various cancers and neurodegenerative disorders. Histone-specific biosensors are key in detecting and quantifying them, advancing our understanding of chromatin dynamics and epigenetic regulation for potential breakthroughs in cancer research and personalized ...
Jeffrey, Barahona +6 more
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