A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Aghaei Nezhad Ehsan; Taimourei Yansary Ramazan; RIAHI ALI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Health and Biomedical Informatics Year:2019 | Volume:6 | Issue:2 Page(s): 101-110

Download Full-Text

Persian Verion

View:

174

Download:

Cites:

Information Journal Paper

Title

A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds

Author(s)

Aghaei Nezhad Ehsan | Taimourei Yansary Ramazan | RIAHI ALI | Issue Writer Certificate

Keywords

Cardiac abnormalities

Heart rate processing

Fuzzy systems

Hummingbird algorithm

Abstract

Introduction: Different factors are effective in detecting heart abnormalities. The greater the number of these factors, the greater the uncertainty in the detection of heart abnormalities. In the uncertainty condition in response of prediction model, the Fuzzy systems are one of the most effective methods for generating an acceptable response. Method: In this applied study, 3240 records related to heart abnormalities were reviewed, each record contained heart sounds of healthy and unhealthy groups. Then, using fuzzy system, the rules of data for the input samples were extracted and the rules were used to categorize the heart abnormalities. Due to the dependency of the effective factors on heart abnormalities, many identical rules with a similar function that result in additional processing and reduced efficacy, will be produced. In the proposed method, the Hummingbird algorithm were used to choose the optimal output rules. Then, using the optimum output rules, the inputs data were categorized into normal and abnormal classes. Data were analyzed using the root mean squared error (RMSE) method. Results: It was revealed that the mean accuracy and time of diagnosis of heart abnormalities in the proposed method were 99. 6% and 0. 56 seconds, respectively, indicating higher efficiency compared to the other similar studies. Conclusion: Compared to the other methods, the proposed model provides more accurate diagnosis and classification.

Cites

No record.

References

No record.

Cite

APA: Copy

Aghaei Nezhad, Ehsan, Taimourei Yansary, Ramazan, & RIAHI, ALI. (2019). A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds. JOURNAL OF HEALTH AND BIOMEDICAL INFORMATICS, 6(2 ), 101-110. SID. https://sid.ir/paper/956853/en

Vancouver: Copy

Aghaei Nezhad Ehsan, Taimourei Yansary Ramazan, RIAHI ALI. A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds. JOURNAL OF HEALTH AND BIOMEDICAL INFORMATICS[Internet]. 2019;6(2 ):101-110. Available from: https://sid.ir/paper/956853/en

IEEE: Copy

Ehsan Aghaei Nezhad, Ramazan Taimourei Yansary, and ALI RIAHI, “A Hybrid Model of Heart Anomalies Detection by Processing Heart Sounds,” JOURNAL OF HEALTH AND BIOMEDICAL INFORMATICS, vol. 6, no. 2 , pp. 101–110, 2019, [Online]. Available: https://sid.ir/paper/956853/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops