A New Adaptive Line Enhancer Based on Singular Spectrum Analysis

Sanei, Saeid; Lee, T. K. M.; Abolghasemi, Vahid

doi:10.1109/tbme.2011.2173936

Cited by 101 publications

(49 citation statements)

References 10 publications

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“…In case of periodic or nearly periodic signals, given the signal period, the information can be used in a way similar to that used in ALE [8]. A delayed (by one period) version of the signal is then used as a reference for adaptive reconstruction of the signal.…”

Section: Adaptive Selection Of Eigen Triples For Groupingmentioning

confidence: 99%

Separation and localisation of heart sound artefacts from respiratory data by adaptive selection of Eigen triples in singular spectrum analysis

Sebastian¹,

Rathnakara

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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Pulmonary auscultation is a vastly using diagnosis method over centuries. Together with the breath sound, there is a possibility of hearing heart sound, since both sounds are originated from the human chest. For the electronic analysis of the breath sound, separation of heart sound (HS) is important. In this paper, the separation of HS is achieved by using a modified Singular Spectrum Analysis (SSA) method, by introducing a provision for adaptive selection of SSA parameters. The advantage is Eigen triple grouping in the reconstruction stage of SSA is adaptive, that reduces the human effort. The performance of the new method is evaluated using synthetically mixed data and the real respiratory data and compared the results with the Advanced Line Enhancer (ALE) method which is an established single channel adaptive method. This method can also be useful for localizing the HS interferences in respiratory data, in some heart sound cancellation technique, where the localization is a fundamental preprocessing step. The comparative results suggest that the proposed method is more suitable for both separation and localization of heart sounds than the original ALE.

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Section: Adaptive Selection Of Eigen Triples For Groupingmentioning

confidence: 99%

Separation and localisation of heart sound artefacts from respiratory data by adaptive selection of Eigen triples in singular spectrum analysis

Sebastian¹,

Rathnakara

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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“…However, there is no general grouping rule. For a specific study, it depends on the research target, the types of signals and noise involved (Azami & Sanei, 2014; Kouchaki, 2014; Sanei, Lee & Abolghasemi, 2012). …”

Section: Introductionmentioning

confidence: 99%

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

Guo

Liu

et al. 2017

PeerJ

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Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).

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“…As such, it is not applicable in scenarios where the signal is in three dimensional (3-D) space, the noise is not white Gaussian, or the artefact signals have temporally correlated components. Sanei et al [3] proposed an ALE which incorporates singular spectrum analysis to alleviate the problems of one dimensional (1-D) ALE. In this paper, the focus is therefore, on developing a new ALE which works for denoising and recovery of 3-D periodic signals.…”

Section: Introductionmentioning

confidence: 99%

Quaternion adaptive line enhancer

Sanei

Took

Enshaeifar

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The recovery of periodic signals from their noisy single channel mixtures has made wide use of the adaptive line enhancer (ALE). The ALE, however, is not designed for detection of two-(2-D) or three-dimensional (3-D) periodic signals such as tremor in an unconstrained hand motion. An ALE which can perform restoration of 3-D periodic signals is therefore required for such purposes. These signals may not exhibit periodicity in a single dimension. To address and solve this problem a quaternion adaptive line enhancer (QALE) is introduced in this paper for the first time which exploits the quaternion least mean square (QLMS) algorithm for the detection of 3-D (extendable to 4-D) periodic signals.Index Terms-ALE, quaternion adaptive line enhancer, QLMS, quaternion shift.

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A New Adaptive Line Enhancer Based on Singular Spectrum Analysis

Cited by 101 publications

References 10 publications

Separation and localisation of heart sound artefacts from respiratory data by adaptive selection of Eigen triples in singular spectrum analysis

Separation and localisation of heart sound artefacts from respiratory data by adaptive selection of Eigen triples in singular spectrum analysis

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

Quaternion adaptive line enhancer

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