Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders.

Body auscultation is a frequent clinical diagnostic procedure used to diagnose heart problems. The key advantage of this clinical method is that it provides a cheap and effective solution that enables medical professionals to interpret heart sounds for the diagnosis of cardiac diseases. Signal proce...

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Publicado en:BioMed Research International pp. 1 - 17
Autores principales: Hossain, Amzad, Uddin, Sharif, Rahman, Parinda, Anee, Meratun Junnut, Rifat, Md Mehedi Hasan, Uddin, M. Monir
Formato: pictorial research tables/charts Journal Article
Publicado: Wiley-Blackwell 7/27/2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 7/27/2022
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      pub: Wiley-Blackwell
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        10.1155/2022/9092346
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        atl: Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders.
      aug:
        au:
          Hossain, Amzad
          Uddin, Sharif
          Rahman, Parinda
          Anee, Meratun Junnut
          Rifat, Md Mehedi Hasan
          Uddin, M. Monir
        affil: Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
      sug:
        subj:
          Heart Diseases Diagnosis
          Heart Murmurs
          Spectral Analysis
          Signal Processing, Computer Assisted
          Health Screening
          Human
          Data Collection Methods
          Descriptive Statistics
          Data Analysis Software
      ab: Body auscultation is a frequent clinical diagnostic procedure used to diagnose heart problems. The key advantage of this clinical method is that it provides a cheap and effective solution that enables medical professionals to interpret heart sounds for the diagnosis of cardiac diseases. Signal processing can quantify the distribution of amplitude and frequency content for diagnostic purposes. In this experiment, the use of signal processing and wavelet analysis in screening cardiac disorders provided enough evidence to distinguish between the heart sounds of a healthy and unhealthy heart. Real-time data was collected using an IoT device, and the noise was reduced using the REES52 sensor. It was found that mean frequency is sufficiently discriminatory to distinguish between a healthy and unhealthy heart, according to features derived from signal amplitude distribution in the time and frequency domain analysis. The results of the present study indicate the adequate discrimination between the characteristics of heart sounds for automatic detection of cardiac problems by signal processing from normal and abnormal heart sounds.
      pubtype: Academic Journal
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        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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