Quantitative comparison of analysis methods for spectroscopic optical coherence tomography.
ABSTRACT: Spectroscopic optical coherence tomography (sOCT) enables the mapping of chromophore concentrations and image contrast enhancement in tissue. Acquisition of depth resolved spectra by sOCT requires analysis methods with optimal spectral/spatial resolution and spectral recovery. In this article, we quantitatively compare the available methods, i.e. the short time Fourier transform (STFT), wavelet transforms, the Wigner-Ville distribution and the dual window method through simulations in tissue-like media. We conclude that all methods suffer from the trade-off in spectral/spatial resolution, and that the STFT is the optimal method for the specific application of the localized quantification of hemoglobin concentration and oxygen saturation.
Project description:We integrate spectroscopic optical coherence tomography (SOCT) with stimulated Raman scattering (SRS) to enable simultaneously multiplexed spatial and spectral imaging with sensitivity to many endogenous biochemical species that play an important role in biology and medicine. The combined approach, termed SRS-SOCT, overcomes the limitations of each individual method. Ultimately, SRS-SOCT has the potential to achieve fast, volumetric, and highly sensitive label-free molecular imaging. We demonstrate the approach by imaging excised human adipose tissue and detecting the lipids' Raman signatures in the high-wavenumber region.
Project description:Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.
Project description:The steady-state motion visual evoked potential (SSMVEP) collected from the scalp suffers from strong noise and is contaminated by artifacts such as the electrooculogram (EOG) and the electromyogram (EMG). Spatial filtering methods can fuse the information of different brain regions, which is beneficial for the enhancement of the active components of the SSMVEP. Traditional spatial filtering methods fuse electroencephalogram (EEG) in the time domain. Based on the idea of image fusion, this study proposed an SSMVEP enhancement method based on time-frequency (T-F) image fusion. The purpose is to fuse SSMVEP in the T-F domain and improve the enhancement effect of the traditional spatial filtering method on SSMVEP active components. Firstly, two electrode signals were transformed from the time domain to the T-F domain via short-time Fourier transform (STFT). The transformed T-F signals can be regarded as T-F images. Then, two T-F images were decomposed via two-dimensional multiscale wavelet decomposition, and both the high-frequency coefficients and low-frequency coefficients of the wavelet were fused by specific fusion rules. The two images were fused into one image via two-dimensional wavelet reconstruction. The fused image was subjected to mean filtering, and finally, the fused time-domain signal was obtained by inverse STFT (ISTFT). The experimental results show that the proposed method has better enhancement effect on SSMVEP active components than the traditional spatial filtering methods. This study indicates that it is feasible to fuse SSMVEP in the T-F domain, which provides a new idea for SSMVEP analysis.
Project description:Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification.
Project description:BACKGROUND: The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R-R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time-frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order--the most crucial factor when using this method--with the help of FFT and WVD methods. RESULTS: Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 +/- 0.003 (mean +/- SD) Hz, 0.076 +/- 0.012 Hz, and 0.117 +/- 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP-RRI phase relationship was found. CONCLUSION: The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04-0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological and computational questions regarding the usability and validity of the low-frequency spectral band when estimating sympathetic activity and baroreflex gain.
Project description:Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal. The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution. The selection of an appropriate window size is difficult when no background information about the input signal is known. In this paper, a novel empirical model is proposed that adaptively adjusts the window size for a narrow band-signal using spectrum sensing technique. For wide-band signals, where a fixed time-frequency resolution is undesirable, the approach adapts the constant Q transform (CQT). Unlike the STFT, the CQT provides a varying time-frequency resolution. This results in a high spectral resolution at low frequencies and high temporal resolution at high frequencies. In this paper, a simple but effective switching framework is provided between both STFT and CQT. The proposed method also allows for the dynamic construction of a filter bank according to user-defined parameters. This helps in reducing redundant entries in the filter bank. Results obtained from the proposed method not only improve the spectrogram visualization but also reduce the computation cost and achieves 87.71% of the appropriate window length selection.
Project description:A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.
Project description:Traditional photon localization microscopy analyses only the spatial distributions of photons emitted by individual molecules to reconstruct super-resolution optical images. Unfortunately, however, the highly valuable spectroscopic information from these photons have been overlooked. Here we report a spectroscopic photon localization microscopy that is capable of capturing the inherent spectroscopic signatures of photons from individual stochastic radiation events. Spectroscopic photon localization microscopy achieved higher spatial resolution than traditional photon localization microscopy through spectral discrimination to identify the photons emitted from individual molecules. As a result, we resolved two fluorescent molecules, which were 15?nm apart, with the corresponding spatial resolution of 10?nm-a four-fold improvement over photon localization microscopy. Using spectroscopic photon localization microscopy, we further demonstrated simultaneous multi-colour super-resolution imaging of microtubules and mitochondria in COS-7 cells and showed that background autofluorescence can be identified through its distinct emission spectra.
Project description:The use of spin-echoes has been employed in an Echo-Planar Spectroscopic Imaging (EPSI) sequence to collect multiple phase encoded lines within a single TR in a Multi-Echo-based Echo-Planar Spectroscopic Imaging technique (MEEPSI). Despite the T₂ dependence on the amplitude of the spin-echoes, the Full Width at Half Maximum (FWHM) of the derived multi-echo Point Spread Function (PSF) is shown to decrease, indicating an improved overall spatial resolution without requiring any additional scan time. The improved spatial resolution is demonstrated in the one-dimensional (1D) spatial profiles of the N-Acetyl Aspartate (NAA) singlet along the phase encode dimension in a gray matter phantom. Although the improved spatial resolution comes at the expense of spectral resolution, it is shown in vivo that peak broadening due to T₂* decay is more significant than the loss of resolution from using spin-echoes and therefore does not affect the ability to quantify metabolites using the LCModel fitting algorithm.