Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring
<p dir="ltr">One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experienc...
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2019
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| author | Muhammad E.H. Chowdhury (17151154) |
| author2 | Amith Khandakar (14151981) Khawla Alzoubi (17987008) Samar Mansoor (18060865) Anas M. Tahir (18060868) Mamun Bin Ibne Reaz (16875933) Nasser Al-Emadi (16864200) |
| author2_role | author author author author author author |
| author_facet | Muhammad E.H. Chowdhury (17151154) Amith Khandakar (14151981) Khawla Alzoubi (17987008) Samar Mansoor (18060865) Anas M. Tahir (18060868) Mamun Bin Ibne Reaz (16875933) Nasser Al-Emadi (16864200) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad E.H. Chowdhury (17151154) Amith Khandakar (14151981) Khawla Alzoubi (17987008) Samar Mansoor (18060865) Anas M. Tahir (18060868) Mamun Bin Ibne Reaz (16875933) Nasser Al-Emadi (16864200) |
| dc.date.none.fl_str_mv | 2019-06-20T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s19122781 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Real-Time_Smart-Digital_Stethoscope_System_for_Heart_Diseases_Monitoring/25295392 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chemical sciences Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware Physical sciences Atomic, molecular and optical physics digital stethoscope heart diseases heart sound machine learning Mel frequency cepstral coefficients (MFCC) features |
| dc.title.none.fl_str_mv | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s19122781" target="_blank">https://dx.doi.org/10.3390/s19122781</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7ca3c414cc6f657cfcb00ffab5db7e90 |
| identifier_str_mv | 10.3390/s19122781 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25295392 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Real-Time Smart-Digital Stethoscope System for Heart Diseases MonitoringMuhammad E.H. Chowdhury (17151154)Amith Khandakar (14151981)Khawla Alzoubi (17987008)Samar Mansoor (18060865)Anas M. Tahir (18060868)Mamun Bin Ibne Reaz (16875933)Nasser Al-Emadi (16864200)Chemical sciencesAnalytical chemistryEngineeringElectrical engineeringElectronics, sensors and digital hardwarePhysical sciencesAtomic, molecular and optical physicsdigital stethoscopeheart diseasesheart soundmachine learningMel frequency cepstral coefficients (MFCC) features<p dir="ltr">One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s19122781" target="_blank">https://dx.doi.org/10.3390/s19122781</a></p>2019-06-20T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s19122781https://figshare.com/articles/journal_contribution/Real-Time_Smart-Digital_Stethoscope_System_for_Heart_Diseases_Monitoring/25295392CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252953922019-06-20T03:00:00Z |
| spellingShingle | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring Muhammad E.H. Chowdhury (17151154) Chemical sciences Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware Physical sciences Atomic, molecular and optical physics digital stethoscope heart diseases heart sound machine learning Mel frequency cepstral coefficients (MFCC) features |
| status_str | publishedVersion |
| title | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| title_full | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| title_fullStr | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| title_full_unstemmed | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| title_short | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| title_sort | Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring |
| topic | Chemical sciences Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware Physical sciences Atomic, molecular and optical physics digital stethoscope heart diseases heart sound machine learning Mel frequency cepstral coefficients (MFCC) features |