Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Abstract

Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.

Possibilities of Eeg Diagnostics for Evaluation of the Massage Effects

“…Gold standard medical measures have been used nowadays in some studies as, for example, blood pressure measures in hypertension studies, but most of the studies relied on self-report measures (Field, 2016). Most of the assessments were based on subjective routine clinical assessment tools, such as 10-point Visual Analogue Scale (VAS) score, the short form McGill Pain Questionnaire (MPQ), state anxiety inventory (STAI), and other questionnaires (Li et al, 2021). Their excessive use may in part relate to limited funding (Field, 2016) or non-invasive nature of measurement.…”

“…Also, they needn't special equipment, different professionals to take and examine the samples or functions. Although these tools have clinical meaning, they are subjective and rough (Li et al, 2021).…”

A Temporal-Spectral-Based Squeeze-and- Excitation Feature Fusion Network for Motor Imagery EEG Decoding

“…In the experiments, we use classification accuracy (ACC) [26], Cohen's kappa coefficient (K) [11], F1-score (F1) [30] and area under curve (AUC) [31] to evaluate the proposed TS-SEFFNet, where the Cohen's kappa coefficient is denoted by:…”