Periodicity transforms

Sethares, William A.; Staley, Thomas W.

doi:10.1109/78.796431

Cited by 143 publications

(98 citation statements)

References 12 publications

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“…The tool has also been integrated into the Staden package for sequence analysis (Kraemer et al 2009). " An alternative to spectral analysis is periodicity transform (PT) that detects repetitive regions in a given sequence as periodicities by decomposing the sequence onto a set of periodic subspaces which represent a sum of periodicities (Sethares and Staley 1999;Muresan and Parks 2003). To biologists, this simply means pictorial presentation of DNA, where a given nucleotide in combination with neighbouring nucleotides constitutes a separate entity.…”

Section: Deterministic Methodsmentioning

confidence: 99%

Searching microsatellites in DNA sequences: approaches used and tools developed

Grover

Aishwarya

Sharma

2011

Physiol Mol Biol Plants

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Microsatellite instability associated genomic activities and evolutionary changes have led to a renewed focus on microsatellite research. In last decade, a number of microsatellite mining tools have been introduced based on different computational approaches. The choice is generally made between slow but exhaustive dynamic programming based approaches, or fast and incomplete heuristic methods. Tools based on stochastic approaches are more popular due to their simplicity and added ornamental features. We have performed a comparative evaluation of the relative efficiency of some microsatellite search tools with their default settings. The graphical user interface, the statistical analysis of the output and ability to mine imperfect repeats are the most important criteria in selecting a tool for a particular investigation. However, none of the available tools alone provides complete and accurate information about microsatellites, and a lot depends on the discretion of the user.

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Section: Deterministic Methodsmentioning

confidence: 99%

Searching microsatellites in DNA sequences: approaches used and tools developed

Grover

Aishwarya

Sharma

2011

Physiol Mol Biol Plants

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“…While the Periodicity Transforms are, in general, robust to modest changes in the magnitudes of the data (Sethares & Staley, 1999), they are less robust to changes in the period. To see why, consider the simple case of a single detected periodicity at (say) p = 10.…”

Section: Choice Of Effective Sampling Ratementioning

confidence: 99%

“…It is also conceptually related to "comb filtering", but it does not use the output of a single filter, rather, it uses the "prongs" of many "combs" to define periodic basis functions that together describe and/or decompose the signal. We have presented an in-depth investigation of the Periodicity Transform in (Sethares & Staley, 1999) and outline the method here.…”

Section: Periodicity Transformsmentioning

confidence: 99%

“…The second idea is to search the (reduced) sound data for periodicities. Originally, we exploited the Fourier Transform for this, but as the limitations of the Fourier analysis became apparent, we developed the Periodicity Transform (which is reviewed in section 3 and fully detailed in (Sethares & Staley, 1999)) that searches directly for periodicities (rather than frequencies, which are inverse periodicities). Thus this is more a method of signal processing at the level of sound waveforms than of symbol manipulation at the note level.…”

Section: Introductionmentioning

confidence: 99%

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Meter and Periodicity in Musical Performance

Sethares¹,

Staley²

2001

Journal of New Music Research

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This paper presents a psychoacoustically based method of data reduction motivated by the desire to analyze the rhythm of musical performances. The resulting information is then analyzed by the "Periodicity Transform" (which is based on a projection onto "periodic subspaces") to locate periodicities in the resulting data. These periodicities represent the rhythm at several levels, including the "pulse", the "measure", and larger structures such as musical "phrases." The implications (and limitations ) of such automated grouping of rhythmic features is discussed. The method is applied to a number of musical examples, its output is compared to that of the Fourier Transform, and both are compared to a more traditional "musical" analysis of the rhythm. Unlike many methods of rhythm analysis, the techniques can be applied directly to the digitized performance (i.e., a soundfile) and do not require a musical score or a MIDI transcription. Several examples are presented that highlight both the strengths and weaknesses of the approach.

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“…They have defined a unified methodological framework of TWA analysis to compare 12 methods with one other, including complex demodulation [9], KL transform [10], Poincaré mapping [11], periodicity transforms [12], the Laplacian likelihood ratio [13], the correlation method [14], and the capon filtering method [15].…”

Section: Previous Research On Quantification Of T-wave Alternansmentioning

confidence: 99%

Fractal-Dimension-Based Method for Quantification of T-Wave Alternans Using Short Time Series

Sakai¹

2013

J Comput Sci Syst Biol

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The presence of T-Wave Alternans (TWA) in an electrocardiogram (ECG) has been certified as an important predictor for the risk of sudden cardiac death (SCD). TWA is a beat-to-beat change in the amplitude of a T-wave, but is rarely visible to the naked eye. Thus, automatic detection and quantification of TWA are desirable. While several automatic algorithms, such as the periodogram method or the modified moving average method (MMA), have been developed to detect or quantify TWA, most conventional methods do not effectively measure short-duration TWA (SDTWA) (<16 beats). In this paper, we proposed a fractal dimension based SDTWA quantification method, and evaluated it with simulated ECG signals with SDTWA episodes (<16 beats) based on the European ST-T database. In the evaluation, the proposed method was applied to ECG signals with TWA amplitude of 5, 15, 30, 45, 60 and 75 μV. Sensitivity and positive predictivity of over or closed to 90% were obtained except for the 5 μV SDTWA episodes. Even for 5 μV SDTWA episodes, the sensitivity reached 75%. We believe that proposed s fractal dimension based method is a promising method for SDTWA analysis.

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Periodicity transforms

Cited by 143 publications

References 12 publications

Searching microsatellites in DNA sequences: approaches used and tools developed

Searching microsatellites in DNA sequences: approaches used and tools developed

Meter and Periodicity in Musical Performance

Fractal-Dimension-Based Method for Quantification of T-Wave Alternans Using Short Time Series

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