Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level
<italic>Goal:</italic> To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. <italic>Methods:&...
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2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10534834/ |
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| author | Chiara Romano Emanuele Maiorana Annunziata Nusca Simone Circhetta Sergio Silvestri Schena Emiliano Gian Paolo Ussia Carlo Massaroni |
| author_facet | Chiara Romano Emanuele Maiorana Annunziata Nusca Simone Circhetta Sergio Silvestri Schena Emiliano Gian Paolo Ussia Carlo Massaroni |
| author_sort | Chiara Romano |
| collection | DOAJ |
| description | <italic>Goal:</italic> To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. <italic>Methods:</italic> SCG and GCG were recorded along three axes at five chest locations of fifteen healthy subjects and AS patients. Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. <italic>Results:</italic> The highest classification accuracies were achieved using Support Vector Machine (SVM) classifier, with the best sensor locations being at the mitral valve for SCG signals (92.3% accuracy) and at the pulmonary valve for GCG (92.1%). Combining SCG and GCG data allows for further improvement in the achievable accuracy (93.5%). Jointly exploiting SCG and GCG signals and both SVM- and ResNet18-based classifiers, 40 s of monitoring allows for reaching 97.2% accuracy with a single sensor on the pulmonary valve. <italic>Conclusions:</italic> Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients. |
| format | Article |
| id | doaj-art-657abf9749854c5289deec37633a93db |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-657abf9749854c5289deec37633a93db2025-02-05T00:01:18ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01586787610.1109/OJEMB.2024.340215110534834Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin LevelChiara Romano0https://orcid.org/0000-0003-3525-0213Emanuele Maiorana1https://orcid.org/0000-0002-4312-6434Annunziata Nusca2https://orcid.org/0000-0002-5616-3197Simone Circhetta3Sergio Silvestri4https://orcid.org/0000-0002-5863-8336Schena Emiliano5https://orcid.org/0000-0002-9696-1265Gian Paolo Ussia6https://orcid.org/0000-0001-8849-3097Carlo Massaroni7https://orcid.org/0000-0002-3090-5623Research Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, ItalyBiometric Systems and Multimedia Forensics (BioMedia4n6) Laboratory of the Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, ItalyFondazione Policlinico Universitario Campus Bio-Medico, Roma, ItalyFondazione Policlinico Universitario Campus Bio-Medico, Roma, ItalyResearch Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, ItalyResearch Unit of Measurements and Biomedical instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, ItalyFondazione Policlinico Universitario Campus Bio-Medico, Roma, ItalyResearch Unit of Measurements and Biomedical instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy<italic>Goal:</italic> To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. <italic>Methods:</italic> SCG and GCG were recorded along three axes at five chest locations of fifteen healthy subjects and AS patients. Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. <italic>Results:</italic> The highest classification accuracies were achieved using Support Vector Machine (SVM) classifier, with the best sensor locations being at the mitral valve for SCG signals (92.3% accuracy) and at the pulmonary valve for GCG (92.1%). Combining SCG and GCG data allows for further improvement in the achievable accuracy (93.5%). Jointly exploiting SCG and GCG signals and both SVM- and ResNet18-based classifiers, 40 s of monitoring allows for reaching 97.2% accuracy with a single sensor on the pulmonary valve. <italic>Conclusions:</italic> Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients.https://ieeexplore.ieee.org/document/10534834/Wearablesseismocardiogramgyrocardiogrammachine learningaortic stenosis (AS)sensors |
| spellingShingle | Chiara Romano Emanuele Maiorana Annunziata Nusca Simone Circhetta Sergio Silvestri Schena Emiliano Gian Paolo Ussia Carlo Massaroni Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level IEEE Open Journal of Engineering in Medicine and Biology Wearables seismocardiogram gyrocardiogram machine learning aortic stenosis (AS) sensors |
| title | Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level |
| title_full | Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level |
| title_fullStr | Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level |
| title_full_unstemmed | Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level |
| title_short | Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level |
| title_sort | classification of aortic stenosis patients via ecg independent multi site measurements of cardiac induced accelerations and angular velocities at the skin level |
| topic | Wearables seismocardiogram gyrocardiogram machine learning aortic stenosis (AS) sensors |
| url | https://ieeexplore.ieee.org/document/10534834/ |
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