Wavelet based watermarked normal and abnormal heart sound identification using spectrogram analysis

Dey, Nilanjan; Mishra, Girish; Nandi, Bijurika; Pal, Moumita; Das, Achintya; Chaudhuri, Sheli Sinha

doi:10.1109/iccic.2012.6510173

Cited by 30 publications

(21 citation statements)

References 9 publications

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“…A separate studies involved computer-aided heart sounds classification for heart disease based on Wavelet Transformation and watermark method for the purpose of telediagnosis [24].…”

Section: Methods Utilized In Heart Sounds Signal Classificationmentioning

confidence: 99%

Diagnostic spectral segregation of cardiac sounds features

Zeljković

Druzgalski

Bojic

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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Globally increasing rate of cardiovascular diseases represents one of the primary health care challenges and necessitates broader population screening for earlier intervention. Present cardiovascular function assessment techniques encompass a broad range of diagnostic tools which in most cases involve high and mid level cost instrumentation. They include among others instrumentation as a part of nuclear cardiology, cardiac catheterization, cardiac ultrasound, relatively basic multiple or simple channel ECG acquisition systems, and other. These techniques and related instrumentation represent very useful and powerful tools to diagnose and/or manage particular cardiac abnormalities; however they are not suitable for broader population low cost basic supplemental cardiac screening. Therefore, these studies focused on a structured assessment of cardiac sound traits as related auscultation is a part of standard medical exams and globally accepted procedures. For these reasons a dedicated cardiac sounds assessment algorithm was developed and has been tested on heart sound database that contains 565 signals encompassing 202 normal and 363 abnormal sounds. A broad range of diverse cardiac sound indicators, versatility of database, and derived characteristics validate potency of possible clinical usefulness and potential for a low cost broader diagnostic screening. In particular, the group of abnormal heart sounds contains samples of 35 different heart abnormalities which are divided into two groups including Group I that contains 28 different heart abnormalities that are 81.81% successfully detectable by the developed algorithm and Group II that contains 7 different heart abnormalities that do not exhibit the frequency change of S1 sound and are not classified by the proposed method. Normal heart sounds in 97.03% of cases were successfully classified with the developed method which is addressing the needs for a selected cardiac screening in light of increasing prevalence of cardiovascular diseases.

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“…A separate studies involved computer-aided heart sounds classification for heart disease based on Wavelet Transformation and watermark method for the purpose of telediagnosis [24].…”

Section: Methods Utilized In Heart Sounds Signal Classificationmentioning

confidence: 99%

Diagnostic spectral segregation of cardiac sounds features

Zeljković

Druzgalski

Bojic

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

View full text Add to dashboard Cite

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“…Imaging techniques are mainly carried out to analyze various physical properties, such as the surface area, number of blood cells, properties of tissue, and the behavior of any organs (Eils & Athale, ). Researchers are interested in the analysis of different types of biomedical images and applications collected from numerous modalities as described in Araki et al, (), Chakraborty, Samanta, Biswas, Dey, and Chaudhuri (), Dey, Roy, Pal, and Das (), Dey, Roy, Das, Das, and Chaudhuri (), Dey, Das, Roy, Das, and Chaudhuri (), Dey, Das, and Chaudhuri (), Dey et al (), Hore et al (). The microscope is one of the main medical image acquisition devices, which has several types including Light‐, Electron‐, Fluorescence‐, and Stimulated Emission Depletion‐microscopy (Pepperkok & Ellenberg, ).…”

Section: Introductionmentioning

confidence: 99%

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

Chakraborty

Chatterjee

Dey³

et al. 2017

Microscopy Res & Technique

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106

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Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR.

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“…The documents come from a patient's electronic medical record and medical problems. The huge amount of publications in medicine [2][3][4][5][6] signifies a massive supply of knowledge, which can efficiently be accessed by developing automated information extraction tools. However, using the strength of computer technology helps people to extract knowledge from large amounts of semi-structured or unstructured texts [7].…”

Section: Introductionmentioning

confidence: 99%

MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

Karâa

Ashour

Sassi³

et al. 2015

Intelligent Systems Reference Library

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MEDLINE is the largest biomedical literature database. It is updated daily with 200-4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.

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Wavelet based watermarked normal and abnormal heart sound identification using spectrogram analysis

Cited by 30 publications

References 9 publications

Diagnostic spectral segregation of cardiac sounds features

Diagnostic spectral segregation of cardiac sounds features

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

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