@article{emrani_saponas_morris_krim_2015, title={A Novel Framework for Pulse Pressure Wave Analysis Using Persistent Homology}, volume={22}, ISSN={["1558-2361"]}, DOI={10.1109/lsp.2015.2441068}, abstractNote={Four characteristic points of pulse pressure waves-the systolic peak, the anacrotic notch, the dicrotic notch, and the diastolic foot-are used to estimate various aspects of cardiovascular function, such as heart rate and augmentation index. We propose a novel approach to extracting these characteristic points using a topological signal processing framework. We characterize the topology of the signals using a collection of persistence intervals, which are encapsulated in a persistence diagram. The characteristic points are identified based on their time of occurrence and their distance from the identity line in the persistence diagram. We validate this approach by collecting radial pulse pressure data from twenty-eight participants using a wearable tonometer, and computing the peripheral augmentation index using a traditional derivative-based method and our novel persistence-based method. The augmentation index values computed using the two methods are statistically indistinguishable, suggesting that this representation merits further exploration as a tool for analyzing pulse pressure waves.}, number={11}, journal={IEEE SIGNAL PROCESSING LETTERS}, author={Emrani, Saba and Saponas, T. Scott and Morris, Dan and Krim, Hamid}, year={2015}, month={Nov} }
 @inproceedings{emrani_krim_2015, title={Spectral estimation in highly transient data}, DOI={10.1109/eusipco.2015.7362678}, abstractNote={We propose a new framework for estimating different frequencies in piece-wise periodic signals with time varying amplitude and phase. Through a 3-dimensional delay embedding of the introduced model, we construct a union of intersecting planes where each plane corresponds to one frequency. The equations of each of these planes only depend on the associated frequency, and are used to calculate the tone in each segment. A sparse subspace clustering technique is utilized to find the segmentation of the data, and the points in each cluster are used to compute the normal vectors. In the presence of white Gaussian noise, principal component analysis is used to robustly perform this computation. Experimental results demonstrate the effectiveness of the proposed framework.}, booktitle={2015 23rd european signal processing conference (eusipco)}, author={Emrani, S. and Krim, H.}, year={2015}, pages={1721–1725} }
 @article{emrani_gentimis_krim_2014, title={Persistent Homology of Delay Embeddings and its Application to Wheeze Detection}, volume={21}, ISSN={["1558-2361"]}, DOI={10.1109/lsp.2014.2305700}, abstractNote={We propose a new approach to detect and quantify the periodic structure of dynamical systems using topological methods. We propose to use delay-coordinate embedding as a tool to detect the presence of harmonic structures by using persistent homology for robust analysis of point clouds of delay-coordinate embeddings. To discover the proper delay, we propose an autocorrelation like (ACL) function of the signals, and apply the introduced topological approach to analyze breathing sound signals for wheeze detection. Experiments have been carried out to substantiate the capabilities of the proposed method.}, number={4}, journal={IEEE SIGNAL PROCESSING LETTERS}, author={Emrani, Saba and Gentimis, Thanos and Krim, Hamid}, year={2014}, month={Apr}, pages={459–463} }
 @inproceedings{emrani_chintakunta_krim_2014, title={Real time detection of harmonic structure: A case for topological signal analysis}, DOI={10.1109/icassp.2014.6854240}, abstractNote={The goal of this study is to find evidence of cyclicity or periodicity in data with low computational complexity and high accuracy. Using delay embeddings, we transform the timedomain signal into a point cloud, whose topology reflects the periodic behavior of the signal. Persistent homology is employed to determine the underlying manifold of the point cloud, and the Euler characteristic provides for a fast computation of topology of the resulting manifold. We apply the introduced approach to breathing sound signals for wheeze detection. Our experiments substantiate the capabilities of the proposed method.}, booktitle={International conference on acoustics speech and signal processing}, author={Emrani, S. and Chintakunta, H. and Krim, H.}, year={2014} }
 @article{emrani_krim_2013, title={Wheeze detection and location using spectro-temporal analysis of lung sounds}, ISSN={["1086-4105"]}, DOI={10.1109/sbec.2013.27}, abstractNote={Wheezes are abnormal lung sounds, which usually imply obstructive airways diseases. The objective of this study is to design an automatic wheeze detector for a wearable health monitoring system, which is able to locate the wheezes inside the respiratory cycle with high accuracy, and low computational complexity. We compute important features of wheezes, which we classify as temporal and spectral characteristics and employed to analyze recorded lung sounds including wheezes from patients with asthma. Time-frequency (TF) technique as well as wavelet packet decomposition (WPD) is used for this purpose. Experimental results verify the promising performance of described methods.}, journal={29TH SOUTHERN BIOMEDICAL ENGINEERING CONFERENCE (SBEC 2013)}, author={Emrani, Saba and Krim, Hamid}, year={2013}, pages={37–38} }