Myofascial Pain Syndrome and Fibromyalgia

Cristancho, Maria M.; Subieta, Gunar B.; Torres, Maria

doi:10.1007/978-981-15-2933-7_21

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Fibromyalgia, a chronic pain syndrome, is associated with pain [1,2] in various body regions, especially in muscles, connective tissue, and around joints [3], as well as fatigue and difficulty with memory, concentration, and sleep [4,5]. The cause of fibromyalgia is unclear, although genetic predisposition, physical or emotional trauma, various infections, and hormonal imbalances have been implicated as triggers [6,7], which may vary among patients [8].…”

Section: A Backgroundmentioning

confidence: 99%

“…To overcome the limited performance of non-deep learning models, we proposed innovative feature engineering methods. Our model comprised four phases: (1) feature extraction that combined multiple filters-based multilevel discrete wavelet transform (MFMDWT) pre-processing [26] with a novel local binary pattern (LBP) [27]-like textural feature generator, 3LBP; (2) feature selection by neighborhood component analysis (NCA) [28] and Chisquare (Chi2) [29] functions; (3) classification with standard shallow k-nearest neighbors (kNN) [30] and support vector machine (SVM) [31] classifiers; and (4) information fusion using mode function-based iterative majority voting (IMV) [32]. Of note, MFMDWT enabled multilevel feature extraction in both the spatial and frequency domains from the decomposed wavelet sub-bands.…”

Section: Motivations and Our Modelmentioning

confidence: 99%

See 1 more Smart Citation

Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal

Barua,

Kobayashi,

Tanabe

et al. 2023

IEEE Access

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Background and Purpose: Fibromyalgia is a chronic pain syndrome associated with sleep disturbances, which may manifest as altered electroencephalography and electrocardiography (ECG) signal alterations during sleep. We aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. Materials and Methods: We analyzed 139 single-lead ECGs recorded during Stage 2 and Sleep Stage 3 of 16 patients with fibromyalgia and 16 age and sex matched controls. ECG records were divided into 15-second segments: 3308 and 1783 in healthy vs fibromyalgia classes, respectively. Our model comprised (1) feature extraction that combined an 8-wavelet filter and 4-level multiple filters-based multilevel discrete wavelet transform signal decomposition with a novel local binary pattern (LBP)-like function, 3LBP, that generated multiple patterns (analogous to quantum superposition) for feature map value extraction (the optimal inputspecific pattern was dynamically selected using a novel forward-forward algorithm); (2) feature selection using neighborhood component analysis and Chi-square functions; (3) classification with k-nearest neighbors and support vector machine classifiers using leave-one-record-out cross-validation; and ( 4) mode functionbased iterative majority voting to generate voted results, from which the best model result was derived. Results: Our model attained binary classification accuracies of 93.87% and 92.02% for Sleep Stage 2 and Sleep Stage 3, respectively. Conclusions: The results and findings clearly illustrate that our proposal distinguish the ECG of fibromyalgia patients from the healthy control patients. The model is self-organized and computationally lightweight, which should facilitate its clinical implementation. INDEX TERMS ECG-based fibromyalgia detection; 3LBP; multiple filters-based multilevel discrete wavelet transform; leave-one-record-out cross-validation; quantum-based feature extraction.

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