TY - GEN
T1 - KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method
AU - Ma, Chunyi
AU - Yang, Jianhua
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Knee Osteoarthritis (KOA) is a chronic degenerative lesion of the knee joint, which is difficult to detect clinically. However, the use of knee-joint vibroarthrographic (VAG) signal to assist clinical testing is an effective non-invasive method for KOA pathology diagnosis. In order to improve the correct rate of pathological diagnosis with VAG signals, this paper proposes a KOA pathological screening method using the multi-channel signal fusion method. This method can realize the VAG signal fusion with both high channel consistency and reliability, increase the difference of normal and abnormal VAG signal characteristics realized on the data, improve the KOA pathology screening accuracy by combining time-domain feature extraction and random forest classification methods to obtain features with more discrimination to categories when extracting features. The experimental results showed that this method could obtain high correct rates of KOA pathology screening, with accuracy, precision, and specificity of 0.95, 0.95, and 0.96, respectively, which can be used as an effective method for non-invasive computer-aided KOA diagnosis.
AB - Knee Osteoarthritis (KOA) is a chronic degenerative lesion of the knee joint, which is difficult to detect clinically. However, the use of knee-joint vibroarthrographic (VAG) signal to assist clinical testing is an effective non-invasive method for KOA pathology diagnosis. In order to improve the correct rate of pathological diagnosis with VAG signals, this paper proposes a KOA pathological screening method using the multi-channel signal fusion method. This method can realize the VAG signal fusion with both high channel consistency and reliability, increase the difference of normal and abnormal VAG signal characteristics realized on the data, improve the KOA pathology screening accuracy by combining time-domain feature extraction and random forest classification methods to obtain features with more discrimination to categories when extracting features. The experimental results showed that this method could obtain high correct rates of KOA pathology screening, with accuracy, precision, and specificity of 0.95, 0.95, and 0.96, respectively, which can be used as an effective method for non-invasive computer-aided KOA diagnosis.
KW - Feature extraction
KW - Knee Osteoarthritis (KOA)
KW - Multi-channel signal fusion
KW - Pathological screening
KW - Random forest
KW - Vibroarthrographic signal
UR - http://www.scopus.com/inward/record.url?scp=85145018681&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21340-3_8
DO - 10.1007/978-3-031-21340-3_8
M3 - 会议稿件
AN - SCOPUS:85145018681
SN - 9783031213397
T3 - Communications in Computer and Information Science
SP - 79
EP - 92
BT - Information Technologies and Intelligent Decision Making Systems - 1st International Conference, ITIDMS 2021, Revised Selected Papers
A2 - Gibadullin, Arthur
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2021
Y2 - 25 January 2021 through 25 January 2021
ER -