ECG compression method using Lorentzian functions model
Digital Signal Processing
A polynomial approximation algorithm for real-time maximum-likelihood estimation
IEEE Transactions on Signal Processing
Estimation of the frequency and decay factor of a decaying exponential in noise
IEEE Transactions on Signal Processing
Estimation of parameters of the weakly damped sinusoidal signals in the frequency domain
Computer Standards & Interfaces
Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data
Computers and Electrical Engineering
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We present fast maximum likelihood (FML) estimation of parameters of multiple exponentially damped sinusoids. The FML algorithm was motivated by the desire to analyze data that have many closely spaced components, such as the NMR spectroscopy data of human blood plasma. The computational efficiency of FML lies in reducing the multidimensional search involved in ML estimation into multiple 1-D searches. This is achieved by using our knowledge of the shape of the compressed likelihood function (CLF) in the parameter space. The proposed FML algorithm is an iterative method that decomposes the original data into its constituent signal components and estimates the parameters of the individual components efficiently using our knowledge of the shape of the CLF. The other striking features of the proposed algorithm are that it provides procedures for initialization, has a fast converging iteration stage, and makes use of the information extracted in preliminary iterations to segment the data suitably to increase the effective signal-to-noise ratio (SNR). The computational complexity and the performance of the proposed algorithm are compared with other existing methods such as those based on linear prediction, KiSS/IQML, alternating projections (AP), and expectation-maximization (EM)