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The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal complexity values for monochords with those for progressive muscle relaxation (PMR), an alternative therapy for relaxation. Forty cancer patients were randomly allocated to one of the two relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were recorded during the first and last sessions. EEG signals were analyzed by applying signal mode complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed a modulation of complexity of beta-2 band (20–29Hz) at midfrontal regions, but only MC group showed a modulation of complexity of theta band (3.5–7.5Hz) at posterior regions. Therefore, the neuronal complexity patterns showed different changes in EEG frequency band specific complexity resulting in two different types of interventions. Moreover, the different neural responses to listening to monochords and PMR were observed after regular relaxation interventions over a short time span.

This study was aimed at the application of a deep graph convolutional neural network (GCNN) in cerebral magnetic resonance imaging (MRI) analysis of patients with depression and the effect of Western medicine combined with music therapy in the treatment of depression. A total of 120 patients with different degrees of depression were divided into the test group with 60 cases (western medicine+music therapy) and the control group with the other 60 cases (western medicine only). All these patients underwent MRI scanning. On the basis of the deep GCNN, an optimized algorithm (O-GCNN) for depression recognition was proposed. It was found that the accuracy, sensitivity, and specificity for classification of the O-GCNN algorithm were significantly higher than those of the convolutional neural network (CNN) model, the back propagation (BP) algorithm, and the forward propagation (FP) algorithm ($P<0.05$). The scores of somatization, interpersonal sensitivity, depression, psychoticism, and anxiety of the test group were significantly lower than those of the control group during and after treatment ($P<0.05$). The scores of the Self-rating Depression Scale (SDS) and Hamilton depression scale (HAMD) of patients in the test group were also significantly lower than those in the control group during and after treatment; the differences were statistically significant ($P<0.05$). The values of left hippocampal regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) of patients in the test group were significantly lower than those in the control group during and after treatment ($P<0.05$). The 24-h urinary free cortisol (UFC) content in the test group was remarkably lower during and after treatment, and the difference was statistically significant ($P<0.05$). The results showed that the improved depression recognition algorithm O-GCNN proposed in this work had a high application value in the auxiliary diagnosis of depression. Music therapy combined with Western medicine treatment can more effectively improve the anxiety and negative mental state of patients with depression and promote the improvement of patients’ conditions.

The need for sentiment analysis in the mental health field is increasing, and electroencephalogram (EEG) signals and music therapy have attracted extensive attention from researchers as breakthrough ideas. However, the existing methods still face the challenge of integrating temporal and spatial features when combining these two types, especially when considering the volume conduction differences among multichannel EEG signals and the different response speeds of subjects; moreover, the precision and accuracy of emotion analysis have yet to be improved. To solve this problem, we integrate the idea of top-k selection into the classic transformer model and construct a novel top-k sparse transformer model. This model captures emotion-related information in a finer way by selecting k data segments from an EEG signal with distinct signal features. However, this optimization process is not without its challenges, and we need to balance the selected k values to ensure that the important features are preserved while avoiding excessive information loss. Experiments conducted on the DEAP dataset demonstrate that our approach achieves significant improvements over other models. By enhancing the sensitivity of the model to the emotion-related information contained in EEG signals, our method achieves an overall emotion classification accuracy improvement and obtains satisfactory results when classifying different emotion dimensions. This study fills a research gap in the field of sentiment analysis involving EEG signals and music therapy, provides a novel and effective method, and is expected to lead to new ideas regarding the application of deep learning in sentiment analysis.

Objective: To explore the effect of Orff vocal therapy on BODE index and quality of life of patients with low lung function after non-small cell lung cancer surgery.

Methods: From March 2014 to March 2019, 231 patients with NSCLC postoperative (their pulmonary functions FEV1 $\le 50\%$ of the expected value) in the Huashan Hospital, Fudan University were selected and randomly divided into observation groups and control group. On the basis of conventional treatment of NSCLC, the control group used lung rehabilitation training, and the observation group used the Orff music therapy. They carry out vocal and breath training step by step, using a combination of online and offline methods, 1h per day online interactive training, 2h of offline group vocal therapy once a week, continuous intervention for 12 months. The patient’s BODE index and FACT-L scores were monthly follow-up.

Results: Compared with the control group, the BODE index and FACT-L scores improved more significantly in the vocal therapy group ($P<0.05$). Especially in the FEV₁pred, family/society module and functional status module, the improvement was most significant, compared with the control group ($P<0.05$). And the loss of follow-up rate in the vocal therapy group was lower than that in the lung rehabilitation group ($P<0.01$).

Conclusion: Orff vocal therapy can improve the BODE index and quality of life of patients with low lung function after non-small cell lung cancer surgery, and its compliance and intervention effects are better than ordinary lung rehabilitation training.

In recent years, modern people have lived a simple and busy life, and they have no other spirits and physical strengths to engage in regular sports. Long-term stress can easily cause autonomic diseases. The heart rate variability analysis has become a functional indicator for the study of the autonomic nervous system that can reflect changes in a person’s health. Therefore, it is necessary to detect stress by autonomic heart rate variability (HRV), and then understand that these symptoms in the original body caused by stress. This research explores the anxiety level change with music therapy on college students. Before music therapy and after music therapy, the degree of strain was measured by the situational anxiety table. The higher the degree of stress in the early stages of measurement is, the more relaxed the students will be after listening to music. However, there is no statistically significant difference in the effect of different levels of anxiety on the effectiveness of music therapy. At the same time, physiological measurements of electrocardiogram and blood pressure were performed before, during, and after the music treatment. The experimental results show that music therapy can effectively reduce the activity of sympathetic nerves and enhance the activity of parasympathetic nerves. It allows students to achieve physical and psychological relaxation after music therapy.

Medical Resonance Therapy Music® originated from Peter Hubner in Germany using the theory of resonance for applications in clinical settings by his multidisciplinary team of professionals. The sonic (20–2000 Hz) and subsonic frequency (0.034–5 Hz) of resonance music is best delivered through headphones with ear muffs in an undisturbed environment 20 minutes or more twice a day. Therapeutic music synchronizes with cellular physiological rhythm, restores neuro-hormonal balance and rekindles the body ability of auto-healing. It helps in restoring different physiological functions, including the nervous, circulatory, digestive and endocrine systems that have their own rhythms. With the appropriate frequency of music, users enter a state of relaxation that guides the body to adjust its pace to deal with stress, insomnia, anxiety, migraine headache, hypertension, hormonal and immune disorders and other clinical problems. The best known examples were serving victims of the Chernobyl nuclear plant incident and children with hypertension. In Germany nowadays, patients can buy Medical Resonance Therapy Music® CDs from pharmacies based on physician prescriptions for 18 types of common medical symptoms and problems, supported by over five years of positive clinical and scientific evidence.

Medical Resonance Therapy Music® should not be overlooked as its new developments have already expanded to Chinese Medicine like “acu-music” (music of different Meridians), Chiropractic practice (music for Spine health), Ayurvedic Medicine (music of Chakras energy centres), mindfulness based meditation (music of meditation) and education (whole brain nurture, creativity and intelligence). The strength of Western Medicine lies in cure of infectious diseases, surgeries and emergency life-saving. Chinese Medicine is strong in managing sub-health before diseases emerge. Natural Medicine goes back to basics of nature and adopts our self-healing power to promote health. However, Medical Resonance Therapy Music® should be considered as a complementary therapy of health problems, but not replacing evidence-based Western Medicines.

The study of autism spectrum disorder (ASD) fascinates various professional disciplines. Music therapy is one of the frontiers in autism research, offering personalized interventions to this specific population and their families. Recent findings provide statistically significant evidence that music therapy interventions are effective for increasing prominent skill areas in young children with ASD, while documentation of additional areas and interventions for a broader age range is emerging. This chapter offers an overview of the impact of music as therapy as well as current music therapy practices, reviews research evidence pertinent to intervention outcomes for individuals with ASD, highlights evolving research and initiatives that are on the horizon, and provides future directions for building on its potential to be an invaluable part of treatment.