Relevant bibliographies by topics / Signal processing Electric filters

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  2. Dissertations / Theses
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Academic literature on the topic 'Signal processing Electric filters'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Signal processing Electric filters"

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Smolarik, Lukas, Dusan Mudroncik, and Lubos Ondriga. "ECG Signal Processing." Advanced Materials Research 749 (August 2013): 394–400. http://dx.doi.org/10.4028/www.scientific.net/amr.749.394.

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Electrocardiography (ECG) is a diagnostic method that allows sensing and record the electric activity of heart [. The measurement of electrical activity is used as a standard twelve-point system. At each of these leads to measure the useful signal and interference was measured. The intensity of interference depends on the artefacts (electrical lines, brum, motion artefacts, muscle, interference from the environment, etc.). For correct evaluation of measured signal there is a need to processing the measured signal to suitable form. At present, the use of electrocardiograms with sensors with contact scanning are difficult to set a time so we decided to use the principle of non-contact sensing. Such a device to measure the ECG was constructed under the project. The disadvantage of such devices is a problem with a high level of noise, which degrades a useful signal. The aim of this article is to pre-process the signals obtained from non-contact sensing. The contactless devices are powered from the network and battery. The electrodes were connected by way of Eithoven bipolar leads. Signals were pre-treated with suitable filters so that they are also appropriate for their subsequent analysis. In the filtration ECG signals was used as a method of linear (low pass filter, high pass, IIR (Infinite Impulse Response) peak, notch filter. The results of many signals clearly demonstrate removing noise in the ECG signals to the point that is also suitable for their analysis.
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Gadsden, S. A., M. Al-Shabi, and S. R. Habibi. "Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle." ISRN Signal Processing 2011 (April 13, 2011): 1–17. http://dx.doi.org/10.5402/2011/120351.

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This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.
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HAAVISTO, P., M. GABBOUJ, and Y. NEUVO. "MEDIAN BASED IDEMPOTENT FILTERS." Journal of Circuits, Systems and Computers 01, no. 02 (June 1991): 125–48. http://dx.doi.org/10.1142/s0218126691000021.

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Idempotent filters produce a root signal in a single filter pass, i.e. the filter output is invariant to further filterings with the same filter. In this paper median based idempotent filter structures are introduced. Two approaches to generate these filters are studied: weighted median filters and median filter cascades. Two subclasses of n-dimensional idempotent weighted median filters, called Class 1 and Class 2 filters in the paper, are introduced. It is shown that both Class 1 and Class 2 filters suppress impulsive noise from n-dimensional input signals and yet have almost no effect on the non-corrupted parts of the signal. These filters are therefore well-suited for example for preprocessing purposes. An application to speech processing is described. Other likely applications of these filters are in image processing and, also, in image sequence processing, where the filter mask is typically 3-dimensional. Sufficient conditions for a filter cascade to be idempotent are given. Two idempotent median filter cascades and their advantages are discussed.
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Vydmysh, Andriy, Oleksandr Voznyak, Igor Kupchuk, and Dmitry Boyko. "RESEARCH OF MEDIAN FILTERING OF ONE-DIMENSIONAL SIGNALS." Vibrations in engineering and technology, no. 1(96) (August 27, 2020): 88–102. http://dx.doi.org/10.37128/2306-8744-2020-1-10.

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This paper considers the principles of digital signal processing, the general provisions of digital filtering, existing methods of noise filtering in electrical signals, the median filters of one-dimensional signals are studied in detail. To solve this problem, the classification of digital signal processing tools is presented. Since the most effective for filtering noise in electrical signals are digital filters, they are given the most attention. The main purpose of signal filtering is the need to extract the information contained in them. This information, which is usually present in the amplitude of the signal (absolute or relative), in frequency or spectral composition, in phase or in the relative time dependences of several signals. The classification of existing digital filters is carried out. For further development, a median filter was selected, which belongs to the class of heuristics and is one of the most effective in filtering signals from impulse noise and white noise. Highlighting the advantages and disadvantages, a review of existing software that implements the median filter. It is established that the urgent task is to increase the processing speed and reduce resource costs in the implementation of such filters, developed an algorithm for fast median filtering, conducted an experimental test of software-implemented median filters with different apertures at different levels of fluctuation noise. This program meets all the requirements of modern norms and standards, allows its practical use to solve real problems of signal processing. In order to increase the speed of information processing, a median filtering algorithm based on difference matrices using the threshold saturation function has been developed. Developed software that implements the proposed algorithm. Schemes of the main program, reading of values of a signal from a file, filtering, sorting of data on amplitude, a choice of a window of elements, a choice of the registered values are presented. The conditions of data registration and ADC parameters to ensure efficient operation of filters are also defined
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Chhavi Saxena, Dr, Dr Avinash Sharma, Dr Rahul Srivastav, and Dr Hemant Kumar Gupta. "Denoising of Ecg Signals Using Fir & Iir Filter: a Performance Analysis." International Journal of Engineering & Technology 7, no. 4.12 (October 4, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i4.12.20982.

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Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.
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Stewart, R. W., J. J. Soraghan, and T. S. Durrani. "Noncanonical FIR filters and adaptive signal processing." Electronics Letters 25, no. 6 (1989): 414. http://dx.doi.org/10.1049/el:19890285.

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Ruiz-Alzola, J., C. Alberola-López, and C. F. Westin. "Kriging filters for multidimensional signal processing." Signal Processing 85, no. 2 (February 2005): 413–39. http://dx.doi.org/10.1016/j.sigpro.2004.09.009.

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Terrell, T. J. "Book Review: Digital Filters and Signal Processing." International Journal of Electrical Engineering & Education 24, no. 1 (January 1987): 86. http://dx.doi.org/10.1177/002072098702400123.

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Havlicek, J. P., G. R. Katz, and J. C. McKeeman. "Even length median filters in optimal signal processing." Electronics Letters 28, no. 13 (1992): 1258. http://dx.doi.org/10.1049/el:19920795.

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Morency, Matthew W., and Geert Leus. "Graphon Filters: Graph Signal Processing in the Limit." IEEE Transactions on Signal Processing 69 (2021): 1740–54. http://dx.doi.org/10.1109/tsp.2021.3061575.

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Dissertations / Theses on the topic "Signal processing Electric filters"

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Orcutt, Edward Kerry 1964. "Correlation filters for time domain signal processing." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/277215.

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This study proposes employing new filters in various configurations for use in digital communication systems. We believe that significant improvements in such performance areas as transmission rate and synchronization may be achieved by incorporating these filters into digital communications receivers. Recently reported in the literature, these filters may offer advantages over the matched filter which allow enhancements in data rates, ISI tolerance, and synchronization. To make full use of the benefits of these filters, we introduce the concept of parallel signal transmission over a single channel. We also examine the effects of signal set selection and noise on performance.
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Tsim, Man-tat Jimmy. "High speed realisation of digital filters /." [Hong Kong] : University of Hong Kong, 1989. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12374088.

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Law, Ying Man. "Iterative algorithms for the constrained design of filters and filter banks /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20LAW.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 108-111). Also available in electronic version. Access restricted to campus users.
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Wepman, Jeffery Alan. "THE MODELING AND ANALYSIS OF AN AUTOMATICALLY TUNED FILTER." Thesis, The University of Arizona, 1985. http://hdl.handle.net/10150/275276.

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詹文達 and Man-tat Jimmy Tsim. "High speed realisation of digital filters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31208939.

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Li, Min. "Induced norm optimal multirate filter bank design using LMI constraints /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20LI.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 55-58). Also available in electronic version. Access restricted to campus users.
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Van, Duyne Scott A. "Digital filter applications to modeling wave propagation in springs, strings, membranes and acoustical space /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Kwan, Wai Ming Hercule. "Parallel implementation of a fast third-order volterra digital filter /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Karam, Lina J. "Design of complex digital FIR filters in the chebyshev sense." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/22219.

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Luo, Yi. "Theory and design of M-channel cosine modulated filter banks and wavelets /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19471130.

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Books on the topic "Signal processing Electric filters"

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Thomas, H. Michael. Analog signal processing. Needham Heights, MA: Simon & Schuster Custom Pub., 1991.

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Signal processing design techniques. Blue Ridge Summit, PA: TAB Professional and Reference Books, 1986.

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Digital filters and signal processing. Boston: Kluwer Academic Publishers, 1986.

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Digital filters and signal processing. 2nd ed. Boston: Kluwer Academic Publishers, 1989.

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Kunt, M. Digital signal processing. Norwood, MA: Artech House, 1986.

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Kunt, M. Digital signal processing. Norwood, MA: Artech House, 1986.

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Hamdy, Nadder A. Applied signal processing: Concepts, circuits, and systems. Boca Raton: Taylor & Francis, 2008.

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Najim, Mohamed. Modeling, estimation and optimal filtration in signal processing. Hoboken, NJ: ISTE ; J. Wiley & Sons, 2008.

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Chirlian, Paul M. Signals and filters. New York: Van Nostrand Reinhold, 1994.

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Design and analysis of analog filters: A signal processing perspective. Boston: Kluwer Academic Publishers, 2001.

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Book chapters on the topic "Signal processing Electric filters"

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Simonik, Petr, and Pavel Brandstetter. "Signal Processing for Active Power Filters." In Lecture Notes in Electrical Engineering, 567–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25646-2_74.

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Mulgrew, Bernard. "Adaptive filters." In Digital Signal Processing, 213–45. London: Macmillan Education UK, 2003. http://dx.doi.org/10.1057/978-1-137-44655-8_8.

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Terrell, Trevor J., and Lik-Kwan Shark. "Digital Filters." In Digital Signal Processing, 208–55. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13735-0_4.

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Mulgrew, Bernard, Peter Grant, and John Thompson. "Adaptive filters." In Digital Signal Processing, 206–39. London: Macmillan Education UK, 1999. http://dx.doi.org/10.1007/978-1-349-14944-5_8.

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Rao, K. Deergha, and M. N. S. Swamy. "Adaptive Digital Filters." In Digital Signal Processing, 693–719. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8081-4_11.

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Alexander, S. Thomas. "Fast Transversal Filters." In Adaptive Signal Processing, 154–76. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8_11.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Adaptive Image Filters." In Digital Signal Processing, 107–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_3.

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Sundararajan, D. "Infinite Impulse Response Filters." In Digital Signal Processing, 251–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62368-5_7.

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Sundararajan, D. "Finite Impulse Response Filters." In Digital Signal Processing, 189–249. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62368-5_6.

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Bjor, Ole-Herman. "Filters." In Handbook of Signal Processing in Acoustics, 107–23. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_7.

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Conference papers on the topic "Signal processing Electric filters"

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Lin, L., C. T. C. Nguyen, R. T. Howe, and A. P. Pisano. "Microelectromechanical filters for signal processing." In [1992] Proceedings IEEE Micro Electro Mechanical Systems. IEEE, 1992. http://dx.doi.org/10.1109/memsys.1992.187722.

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Wang, Cheng, Chuanxue Song, and Jianhua Li. "A federated filter design of electronic stability control for electric-wheel vehicle." In 2015 8th International Congress on Image and Signal Processing (CISP). IEEE, 2015. http://dx.doi.org/10.1109/cisp.2015.7408045.

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Liye, Zhao, and Li Hongsheng. "Adaptive morphological wavelet filter used in gravimeter signal processing." In 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC). IEEE, 2011. http://dx.doi.org/10.1109/mec.2011.6026008.

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Warman, Rahmat Aditya, Sunu Wibirama, and Agus Bejo. "Performance comparison of signal processing filters on smooth pursuit eye movements." In 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285477.

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Asif, Amir, Arash Mohammadi, and Shivam Saxena. "Reduced order distributed particle filter for electric power grids." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855080.

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Dewasthale, Mugdha M., and R. D. Kharadkar. "Acoustic Noise Cancellation Using Adaptive Filters: A Survey." In 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC). IEEE, 2014. http://dx.doi.org/10.1109/icesc.2014.11.

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Dean, Robert, George Flowers, Nicole Sanders, Ken MacAllister, Roland Horvath, A. S. Hodel, Wayne Johnson, Michael Kranz, and Michael Whitley. "Damping control of micromachined lowpass mechanical vibration isolation filters using electrostatic actuation with electronic signal processing." In Smart Structures and Materials, edited by Kon-Well Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.599946.

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Altay, Yeldos A., and Artem S. Kremlev. "Signal-to-Noise Ratio and Mean Square Error Improving Algorithms Based on Newton Filters for Measurement ECG Data Processing." In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. http://dx.doi.org/10.1109/elconrus51938.2021.9396391.

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Park, Simon S., and Y. Altintas. "Dynamic Compensation of Cutting Forces Measured From the Spindle Integrated Force Sensor System." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32037.

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This paper presents a dynamically compensated Spindle Integrated Force Sensor (SIFS) system to measure cutting forces. Piezo-electric force sensors are integrated to the stationary spindle housing. The structural dynamic model between the cutting forces acting on the tool tip and the measured forces at the spindle housing is identified. The system is first calibrated to compensate the influence of spindle run-out and unbalance at different speeds. Using both the cutting force and acceleration sensor signals measured at the spindle housing, a Kalman Filter is designed to filter the influence of structural modes on the force measurements. The frequency bandwidth of the proposed sensor system is expanded from 300 Hz to about 800 Hz with the proposed sensing and the signal processing method.
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Baala Sundaram, Divaakar Siva, Anoushka Kapoor, Jackie Xie, Natalie C. Xu, Prissha Krishna Moorthy, Rogith Balasubramani, Suganti Shivaram, Anjani Muthyala, and Shivaram Poigai Arunachalam. "Comparison of Multiscale Frequency Characteristics of Normal Phonocardiogram With Diseased Heart States." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9090.

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Abstract Phonocardiogram (PCG) signals are electrical recording of heart sounds containing vital information of diagnostic importance. Several signal processing methods exist to characterize PCG, however suffers in terms of sensitivity and specificity in accurately discriminating normal and abnormal heart sounds. Recently, a multiscale frequency (MSF) analysis of normal PCG was reported to characterize subtle frequency content changes in PCG which can aid in differentiating normal and abnormal heart sounds. In this work, it was hypothesized that MSF can discriminate normal PCG signal compared to an artifact, PCG with extra systolic heart sounds and murmur based on their varying frequency content. Various samples of PCG with normal and abnormal heart sounds were obtained from Peter Bentley Heart Sounds Database sampled at 44.1 kHz for analysis. The signal was filtered using a 4th order Butterworth lowpass filter with cutoff frequency at 200 Hz to remove higher frequency noise and MSF estimation was performed on the filtered dataset using custom MATLAB software. Mann-Whitney test was performed for statistical significance at p < 0.05. Results indicate that MSF successfully discriminated normal and abnormal heart sounds, which can aid in PCG classification with more sophisticated analysis. Validation of this technique with larger dataset is required.
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