Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony
Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-01-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076221150741 |
| _version_ | 1797954659143385088 |
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| author | Pantea Keikhosrokiani A. Bhanupriya Naidu A/P Anathan Suzi Iryanti Fadilah Selvakumar Manickam Zuoyong Li |
| author_facet | Pantea Keikhosrokiani A. Bhanupriya Naidu A/P Anathan Suzi Iryanti Fadilah Selvakumar Manickam Zuoyong Li |
| author_sort | Pantea Keikhosrokiani |
| collection | DOAJ |
| description | Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages. |
| first_indexed | 2024-04-10T23:22:03Z |
| format | Article |
| id | doaj.art-735e1ec853ac4989b10123fdbb8a52fa |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-04-10T23:22:03Z |
| publishDate | 2023-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-735e1ec853ac4989b10123fdbb8a52fa2023-01-12T14:03:23ZengSAGE PublishingDigital Health2055-20762023-01-01910.1177/20552076221150741Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colonyPantea Keikhosrokiani0A. Bhanupriya Naidu A/P Anathan1 Suzi Iryanti Fadilah2Selvakumar Manickam3Zuoyong Li4 School of Computer Sciences, , Minden, Penang, Malaysia School of Computer Sciences, , Minden, Penang, Malaysia School of Computer Sciences, , Minden, Penang, Malaysia National Advanced IPv6 Centre, , Minden, Penang, Malaysia College of Computer and Control Engineering, , Fuzhou, ChinaCardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.https://doi.org/10.1177/20552076221150741 |
| spellingShingle | Pantea Keikhosrokiani A. Bhanupriya Naidu A/P Anathan Suzi Iryanti Fadilah Selvakumar Manickam Zuoyong Li Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony Digital Health |
| title | Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony |
| title_full | Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony |
| title_fullStr | Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony |
| title_full_unstemmed | Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony |
| title_short | Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony |
| title_sort | heartbeat sound classification using a hybrid adaptive neuro fuzzy inferences system anfis and artificial bee colony |
| url | https://doi.org/10.1177/20552076221150741 |
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