Gaussian mixture model in clustering acoustic emission signals for characterizing osteoarthritic knees

Khan, Tawhidul Islam; Sakib, Nazmush; Hassan, Md. Mehedi; Ide, Shuya

doi:10.1016/j.bspc.2023.105510

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent research demonstrated the effectiveness of feature extraction libraries such as Time Series Feature Extraction Library (TSFEL) in extracting temporal and spatial data for multidimensional signal feature extraction [ 18 ]. The Gaussian mixture model (GMM) with principal component analysis (PCA) has found successful implementation in clustering and diagnosing knee osteoarthritis from acoustic emission signals [ 19 ]. Additionally, the K-nearest neighbour method remains a popular choice for clustering and classification in EMG data [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Farook,

Haq,

Ramees

et al. 2024

Med Biol Eng Comput

View full text Add to dashboard Cite

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity. Graphical Abstract

show abstract

Section: Introductionmentioning

confidence: 99%