Biology subjects

Babaev, B.

Publications and source records attributed to Babaev, B..

3 recordsLinked to original sources

bioRxiv2026

Impedance-Derived Heart Rate and Heart Rate Variability from tDCS Output Voltage: Sensorless Physiological Monitoring During Electrical Neuromodulation

BackgroundTranscranial direct current stimulation (tDCS) devices adjust output voltage to maintain the target current despite varying impedance. Pulsatile blood flow produces beat-synchronous changes in tissue impedance. ObjectiveTo determine whether impedance-derived heart rate (IHR), heart rate variability (IHRV), and respiration (IDR) can be estimated from tDCS output voltage without additional physiological sensors. MethodsA custom analog front-end acquires the tDCS output voltage across its full dynamic DC range and superimposed AC fluctuations with high precision. Beats detected from the AC-coupled signal yielded normal-to-normal intervals for HR, HRV, and interval-derived respiration. Accuracy was quantified as mean absolute error (MAE) in 10 healthy laboratory participants against ECG and respiration-monitor references, and against chest-strap RR intervals in 19 at-home sessions from 10 participants with mild-to-moderate depression. ResultsLaboratory MAEs versus ECG were 0.57 bpm for HR, 9.40 ms for SDNN, and 18.90 ms for RMSSD (r = 0.995, 0.859, and 0.778; N = 10); respiratory-rate MAE was 1.36 breaths/min (r = 0.852; N = 6). Across 1-5 mA of tDCS, the cardiac voltage {Delta}Vcardiac(t) amplitude scaled linearly with current (slope, 0.073 mV/mA; p < 0.001). The pulsatile impedance {Delta}Zcardiac(t) = ({Delta}Vcardiac(t)/Iapplied) amplitude averaged 0.080 {+/-} 0.029 {Omega} (mean {+/-} SD) across 50 participant-current observations, with no significant dependence on intensity (slope, -0.002 {Omega}/mA; p = 0.086). At-home MAEs were 1.43 bpm for HR, 8.86 ms for SDNN, and 24.92 ms for RMSSD (r = 0.995, 0.767, and 0.680; 19 sessions). ConclusionstDCS output voltage contains a recoverable cardiac-synchronous signal arising from pulsatile impedance, enabling HR, HRV, and respiratory monitoring without additional physiological sensors.

bioengineering↗

bioRxiv2025

High-Capacity transcranial Direct Current Stimulation (HC-tDCS)

BackgroundEnhancing tDCS technology can support the delivery of higher current intensities, enabling broader dose-response studies in human trials. MethodsHigh-Capacity tDCS (HC-tDCS) integrates novel electrodes and adaptive current/voltage controlled electronics. Multi-layer HC electrodes include polarity-specific redox layers and designed hydrogel interfaces, shaped for a bifrontotemporal montage. The stimulator design includes adaptive ramps with hybrid voltage-current control and low (7.5 V) compliance voltage. Scanning electron microscopy (SEM) and electrical impedance spectroscopy (EIS) were used to characterize electrode properties. Tolerability of HD-tDCS was tested for target currents 1-6 mA (in 1 mA increments) for 30 min on 5 healthy subjects, and compared with conventional tDCS using 2 mA F3-F4 sponge-electrodes. MRI-derived computational models predicted cortical electric fields. Tolerability was assessed according to the 100 mm visual analogue scale for pain (VASP-100), skin erythema assessment, thermal imaging, and adverse event questionnaires. ResultsThe electrode design including high-roughness polarity-specific capacity, electrochemically supports high-charge direct current stimulation. In all subjects, HC-tDCS was well tolerated at all tested doses (1-6 mA) with minor transient adverse events and average VASP-100 less than 15. VASP-100 during sponge-electrode tDCS at 2 mA was comparable to 5 and 6 mA HC-tDCS. HC-tDCS operates at significantly lower voltage than sponge-tDCS, impacting tolerability and efficiency. Modeling predicts peak frontal electric fields of 0.65-1.08 V/m for 2 mA HC-tDCS and 1.95-3.25 V/m for 6 mA HC-tDCS, compared to 0.49-0.95 V/m for 2 mA sponge-tDCS. ConclusionsHC-tDCS allows increased cortical stimulation; at 6 mA achieving double the 1 V/m electric field threshold in all subjects. Enabled by pre-stimulation procedures, specialized electrodes, and adaptive low-voltage stimulators, HC-tDCS is well tolerated at intensities up to at least 6 mA.

bioengineering↗

bioRxiv2024

Improved accuracy for estrous cycle staging using supervised object detection

The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle comprises four stages influenced by fluctuating levels of hormones, mainly estradiol and progesterone. Tracking the cycle traditionally relies on vaginal cytology, which categorizes stages based on three epithelial cell concentrations. However, this method has limitations, including time-consuming training and variable accuracy among researchers. To address these challenges, this study assessed the feasibility and reliability of two machine learning methods. An object detection-based machine learning model, Object Detection Estrous Staging (ODES), was employed to identify cell types throughout the estrous cycle in mice. A dataset of 555 vaginal cytology images with four different stains was annotated, with 335 images for training, 45 for validation, and 175 for testing. A novel, accurate set of rules for classification was derived by analyzing training images. ODES achieved an average accuracy of 87% in classifying cycle stages and took only 3.9 minutes to analyze 175 test images. The use of object detection machine learning significantly improved accuracy and efficiency compared to previously derived supervised image classification models (33-45% accuracy) and human accuracy (66% accuracy), refining research practices for female studies. These findings facilitate the integration of the estrous cycle into research, enhancing the quality of scientific results by allowing for efficient and accurate identification of the cycle stage.

animal behavior and cognition↗