Smart VR Mindfulness for Breast Cancer: Integrating Biofeedback and Evaluating Chemotherapy Effectiveness

The Revised Newly Diagnosed Breast Cancer Stress Scale (NDBCSS-R) will be used: This scale consists of 17 items, divided into four parts: Negative Perception (3 items), Threat (4 items), Unpredictability (5 items), and Facing Challenge (4 items). Scoring is based on a 4-point Likert scale, with 3 indicating strong agreement and 0 indicating disagreement. Higher scores indicate a greater level of perceived stress. Items 1, 3, 5, 11, 13, and 15 are reverse-scored.

The Memorial Symptom Assessment Scale (MSAS) will be used: It consists of 32 items. First, participants indicate the presence or absence of symptoms. If symptoms are present, the frequency, severity, and distress of 24 symptoms, and the severity and distress of 8 symptoms, are evaluated. Frequency and severity are assessed using a 4-point scale, with 1 indicating rarely or mild and 4 indicating almost constantly or very severe. If there are no symptoms, the item is scored as 0. Distress is assessed using a 5-point scale, with 0 indicating not at all and 4 indicating very much.

The Cognitive and Affective Mindfulness Scale-Revised (CAMS-R) will be used: It consists of 10 items, rated on a 4-point scale, with 1 indicating almost never and 4 indicating almost always. Higher scores indicate a higher level of mindfulness.

The three-lead electrocardiography (ECG) from the Thought Technology five-channel biofeedback system kit will be used as the measurement tool. Using the NeuroKit2 package in Python, we aim to extract R-peaks from time-series data. Initially, we apply a 60 Hz notch filter to remove powerline noise, followed by a bandpass filter with a frequency range of 0.05 to 100 Hz. To enhance the QRS complex and make the R-peaks more prominent within the signal, we use a dynamic threshold setting based on the signal amplitude from the preceding 15 seconds. Subsequently, we detect the peaks, verify, and correct them. Finally, we calculate the HRV features.

Respiration belts from the Thought Technology five-channel biofeedback system kit will be used to measure changes in chest volume during breathing. Using the NeuroKit2 package in Python, we employ Fast Fourier Transform (FFT) to convert the time-series data to the frequency domain. We set the frequency range to 0.2 to 0.33 Hz to correspond to a normal average breathing rate of 12 to 20 breaths per minute, thereby removing irrelevant noise. The difference method is used to calculate and establish the starting points of inhalation and exhalation, allowing for the calculation of breathing rate and relative breathing depth. A moving average method is then applied to provide a stable mean representation of the breathing rate, eliminating transient anomalies. These processed data are subsequently used to calculate RRV features.

The OpenBCI Ganglion Board will serve as the development platform, with the chip and wet electrodes integrated into a virtual reality headset for user convenience. Bluetooth connectivity will interface with the Pico headset, transmitting EEG signals to the software. A four-channel EEG setup, with electrodes placed according to the international 10-20 system, will be employed. Using the NeuroKit2 package in Python, we preprocess EEG data by initially filtering to remove 60 Hz noise and retain 2 to 30 Hz signals. We then convert the signals to the time-frequency domain using Morlet Wavelet, capturing features across different frequency bands. The power spectral density of each channel is segmented into δ, θ, α, β, and γ bands, and the average power in each band is calculated to analyze and compare brainwave activity intensity.

Skin conductance monitoring electrodes from the Thought Technology five-channel biofeedback system kit will be used. Using the BioSPPy package in Python, we first apply a filter for noise reduction. Then, we detect peaks and extract features.

The enzyme-linked immunosorbent assay (ELISA) will be used for detection. This method is based on the specific binding of antibodies to antigens, combined with a chromogenic substrate reaction. The color intensity is used to quantify the concentration of the specific target analyte in the sample.