Phonocardiogram Event-based Delineation Method Using Continuous Wavelet Transform | Chapter 6 | Engineering Research: Perspectives on Recent Advances Vol. 2

Objective: Time frequency-based methods have been employed for event detection in audio signals.

Methods: The spectrogram can be used to extract loudness and energy functions by taking the row sum. The loudness function has been differentiated to obtain the on-set events of the audio. This concept could be replicated in the case of bio-signals such as phonocardiograms to extract on-set and off-set boundaries. A continuous Wavelet Transform-based Time-Frequency algorithm for on-set and off-set event detection has been developed for Phonocardiograms and this method has been tested on the Phonocardiogram (PCG) dataset hosted in the Physionet repository. To validate the simulation, real implementation has also been carried out with real heart sound recordings obtained from a Bluetooth-based stethoscope.

Results: The simulation carried out using PCG sounds from the Physionet database gave an F1-score of 99.11%. Similar results were found using the real heart sound recordings from stemoscope with an F1- score of 99.30%.

Conclusion: Experimental evaluation and Simulations both indicate that a Continuous Wavelet Transform-based time-frequency algorithm can be used to derive on-set and off-set events of the sounds in the PCG signal.

 

Author (s) Details

 

Vishwanath Madhava Shervegar
Department of Electronics & Communication Engineering, Moodlakatte Institute of Technology, Kundapura, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v2/3276

Determination of Power Quality Events Using DWT and DTCWT Wavelets | Chapter 3 | Research Highlights in Science and Technology Vol. 8

 This chaoter judge PQ event algorithm seeing dual tree wavelets and the results are distinguished with wavelets. Power quality disturbances (PQ) are produce with the growth of nonlinear loads, to a degree solid-state switching supplies, electronically switched ploys, industrial rectifiers, and inverters. Warped voltage waveforms unfavorably affect electronic ploys, such as electrical method failures, disk crashes, and microcontroller breakdowns It is shown that the shift invariant feature of dual tree wavelets is beneficial for classifying events in a variety of PQ signals accompanying non-stationary occurrences. The strength levels of the Dual Tree Complex Wavelet Transform (DTCWT) are able to distinguish between many occurrences as well as various sags, swells, tone of sound, interrupts, and flickers.  The classification veracity using DTCWT energy bands is enhanced by more than 90%. DTCWT filters selected in this place paper are suitable for PQ event discovery as well as classification. The recommendation PQ signal with PQ event to a degree sag, swell, transient, harmonics and flicker happen at random intervals in actual time for action or event. The feature detection algorithm needs to discover the presence of event from the physiognomy detected from sub bands and too characterize the occurrence by providing information on time of incident, event duration, force and gradient.

Author(s) Details:

E. Prathibha,
Adama Science and Technology University, Ethiopia.

A. Manjunatha,
Sri Krishna Institute of Technology, Bangalore, Karnataka, India.

R. Likhitha,
Nitte Meenakshi Institute of Technology, India.

Md. Irfan Ali,
Adama Science and Technology University, Ethiopia.

Please see the link here: https://stm.bookpi.org/RHST-V8/article/view/11539