TY - GEN
T1 - The Classification of Heartbeat PCG Signals via Transfer Learning
AU - Almanifi, Omair Rashed Abdulwareth
AU - Mohd Razman, Mohd Azraai
AU - Musa, Rabiu Muazu
AU - Ahmad, Ahmad Fakhri
AU - Ismail, Muhammad Yusri
AU - P. P. Abdul Majeed, Anwar
N1 - Funding Information:
Acknowledgements The authors would like to acknowledge Universiti Malaysia Pahang for funding this study via RDU180321.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Cardiovascular auscultation is a process of listening to the sound of a heartbeat to pick up on any abnormalities. One of these abnormalities is heart murmurs, which are the result of blood turbulence, in or near the heart. Heart murmurs can be innocent, or they can indicate the existence of very serious diseases. Normally the process is performed with a stethoscope, by a medical professional, where murmurs are identified by the subtle difference in timing and pitch from a normal heartbeat. These professionals, however, are not always available; hence, the need for the automation of this process rises. This paper aims at testing the performance of pre-trained CNN models at the classification of heartbeats. A database of phonocardiogram (PCG) heartbeat recordings, under the name of the PASCAL CHSC database was used to train four pre-trained models: VGG16, VGG19, MobileNet, and inceptionV3. The data was processed, and the features were extracted using Spectrogram signal representation. They were then split into training and testing data, and the results were compared using the metrics of accuracy and loss. The classification accuracies of the VGG16, VGG19, MobileNet, and inceptionV3 models are 80.25%, 85.19%, 72.84% and 54.32%, respectively. The findings of the paper indicate that the use of different transfer learning models can, to a certain extent, enhance the overall accuracy at detecting the murmurs of the heart.
AB - Cardiovascular auscultation is a process of listening to the sound of a heartbeat to pick up on any abnormalities. One of these abnormalities is heart murmurs, which are the result of blood turbulence, in or near the heart. Heart murmurs can be innocent, or they can indicate the existence of very serious diseases. Normally the process is performed with a stethoscope, by a medical professional, where murmurs are identified by the subtle difference in timing and pitch from a normal heartbeat. These professionals, however, are not always available; hence, the need for the automation of this process rises. This paper aims at testing the performance of pre-trained CNN models at the classification of heartbeats. A database of phonocardiogram (PCG) heartbeat recordings, under the name of the PASCAL CHSC database was used to train four pre-trained models: VGG16, VGG19, MobileNet, and inceptionV3. The data was processed, and the features were extracted using Spectrogram signal representation. They were then split into training and testing data, and the results were compared using the metrics of accuracy and loss. The classification accuracies of the VGG16, VGG19, MobileNet, and inceptionV3 models are 80.25%, 85.19%, 72.84% and 54.32%, respectively. The findings of the paper indicate that the use of different transfer learning models can, to a certain extent, enhance the overall accuracy at detecting the murmurs of the heart.
KW - Classification
KW - Feature selection
KW - Heart murmur
KW - Machine learning
KW - PCG
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85112579727&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_5
DO - 10.1007/978-981-33-4597-3_5
M3 - Conference Proceeding
AN - SCOPUS:85112579727
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 49
EP - 59
BT - Recent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Ibrahim, Ahmad Najmuddin
A2 - Ishak, Ismayuzri
A2 - Mat Yahya, Nafrizuan
A2 - Zakaria, Muhammad Aizzat
A2 - P. P. Abdul Majeed, Anwar
PB - Springer Science and Business Media Deutschland GmbH
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
Y2 - 6 August 2020 through 6 August 2020
ER -