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  • Open Access


    Research on the Intervention Effect of Vibroacoustic Therapy in the Treatment of Patients with Depression

    Xiaodan Wang1,*, Zheng Xie2, Guiping Du3

    International Journal of Mental Health Promotion, Vol.26, No.2, pp. 149-160, 2024, DOI:10.32604/ijmhp.2023.030755

    Abstract Research purpose: This study implemented somatosensory music therapy on patients with depressive disorders, and explored the effects of somatosensory music therapy on the degree of depression, positive and negative emotions, intuitive stress and autonomic nervous function of patients. Research method: We collected 66 patients diagnosed with depression from the Department of Psychological Medicine of Henan Provincial People’s Hospital, and divided them into a control group and an intervention group according to the random number table, with 33 people in each group. The control group received routine psychiatric treatment and nursing. On the basis of the control group, the intervention group… More >

  • Open Access


    Main Melody Configuration and Chord Algorithm for Relaxing Music Generation

    Chih-Fang Huang1,*, Ai-Hsien Fan2, Jin-Huang Huang3, Hsing-Cheng Huang3

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 661-673, 2023, DOI:10.32604/iasc.2023.027165

    Abstract This study applies the diatonic chord in music theory, utilization rate, and the close relationship between the main chord system, the dominant chord system, and the subordinate chord system. From the perspective of music theory, the computer can automatically and quickly analyze the music, and establish a set of algorithms for configuring the chord accompaniment for the main melody, called the symmetrical circle of fifths algorithm, SCFA (Symmetrical Circle of Fifths Algorithm). SCFA can immediately confirm the key, perform harmony analysis, configure chord accompaniment for the main melody, and effectively and correctly complete any given melody or interval. It can… More >

  • Open Access


    Noisy ECG Signal Data Transformation to Augment Classification Accuracy

    Iqra Afzal1, Fiaz Majeed1, Muhammad Usman Ali2, Shahzada Khurram3, Akber Abid Gardezi4, Shafiq Ahmad5, Saad Aladyan5, Almetwally M. Mostafa6, Muhammad Shafiq7,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.022711

    Abstract In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The proposed model uses ECG signals… More >

  • Open Access


    Using Mobile Technology to Construct a Network Medical Health Care System

    Sung-Jung Hsiao1, Wen-Tsai Sung2,*

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 729-748, 2022, DOI:10.32604/iasc.2022.020332

    Abstract In this study, a multisensory physiological measurement system was built with wireless transmission technology, using a DSPIC30F4011 as the master control center and equipped with physiological signal acquisition modules such as an electrocardiogram module, blood pressure module, blood oxygen concentration module, and respiratory rate module. The physiological data were transmitted wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth serial ports of Wi-Fi. The Android applications displayed the acquired physiological signals in real time and performed a preliminary abnormity diagnosis based on the measured physiological data and built-in index diagnostic data provided by doctors, such as blood oxygen concentration,… More >

  • Open Access


    Modeling of Heart Rate Variability Using Time-Frequency Representations

    Ghaylen Laouini1, Ibrahim Mahariq1, Thabet Abdeljawad2,3,4,*, Hasan Aksoy5

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1289-1299, 2021, DOI:10.32604/cmc.2021.018411

    Abstract The heart rate variability signal is highly correlated with the respiration even at high workload exercise. It is also known that this phenomenon still exists during increasing exercise. In the current study, we managed to model this correlation during increasing exercise using the time varying integral pulse frequency modulation (TVIPFM) model that relates the mechanical modulation (MM) to the respiration and the cardiac rhythm. This modulation of the autonomic nervous system (ANS) is able to simultaneously decrease sympathetic and increase parasympathetic activity. The TVIPFM model takes into consideration the effect of the increasing exercise test, where the effect of a… More >

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