ConceFT for Time-Varying Heart Rate Variability Analysis as a Measure of Noxious Stimulation During General Anesthesia

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach

ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT. The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.

Theta Oscillations at Subthalamic Region Predicts Hypomania State After Deep Brain Stimulation in Parkinson's Disease

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective treatment for the motor impairments of patients with advanced Parkinson's disease. However, mood or behavioral changes, such as mania, hypomania, and impulsive disorders, can occur postoperatively. It has been suggested that these symptoms are associated with the stimulation of the limbic subregion of the STN. Electrophysiological studies demonstrate that the low-frequency activities in ventral STN are modulated during emotional processing. In this study, we report 22 patients with Parkinson's disease who underwent STN DBS for treatment of motor impairment and presented stimulation-induced mood elevation during initial postoperative programming. The contact at which a euphoric state was elicited by stimulation was termed as the hypomania-inducing contact (HIC) and was further correlated with intraoperative local field potential recorded during the descending of DBS electrodes. The power of four frequency bands, namely, θ (4–7 Hz), α (7–10 Hz), β (13–35 Hz), and γ (40–60 Hz), were determined by a non-linear variation of the spectrogram using the concentration of frequency of time (conceFT). The depth of maximum θ power is located approximately 2 mm below HIC on average and has significant correlation with the location of contacts (r = 0.676, p < 0.001), even after partializing the effect of α and β, respectively (r = 0.474, p = 0.022; r = 0.461, p = 0.027). The occurrence of HIC was not associated with patient-specific characteristics such as age, gender, disease duration, motor or non-motor symptoms before the operation, or improvement after stimulation. Taken together, these data suggest that the location of maximum θ power is associated with the stimulation-induced hypomania and the prediction of θ power is frequency specific. Our results provide further information to refine targeting intraoperatively and select stimulation contacts in programming.

Differentiation of skin incision and laparoscopic trocar insertion via quantifying transient bradycardia measured by electrocardiogram

Most surgical procedures involve structures deeper than the skin. However, the difference in surgical noxious stimulation between skin incision and laparoscopic trocar insertion is unknown. By analyzing instantaneous heart rate (IHR) calculated from the electrocardiogram, in particular the transient bradycardia in response to surgical stimuli, this study investigates surgical noxious stimuli arising from skin incision and laparoscopic trocar insertion, and their difference. Thirty-five patients undergoing laparoscopic cholecystectomy were enrolled in this prospective observational study. Sequential surgical steps including umbilical skin incision (11 mm), umbilical trocar insertion (11 mm), xiphoid skin incision (5 mm), xiphoid trocar insertion (5 mm), subcostal skin incision (3 mm), and subcostal trocar insertion (3 mm) were investigated. IHR was derived from electrocardiography and calculated by the modern time-varying power spectrum. Similar to the classical heart rate variability analysis, the time-varying low frequency power (tvLF), time-varying high frequency power (tvHF), and tvLF-to-tvHF ratio (tvLHR) were calculated. Prediction probability (P_K) analysis and global pointwise F-test were used to compare the statistical performance between indices and the heart rate readings from the patient monitor. Analysis of IHR showed that surgical stimulus elicits a transient bradycardia, followed by the increase of heart rate. Transient bradycardia is more significant in trocar insertion than skin incision (p < 0.001 for tvHF). The IHR change quantifies differential responses to different surgical intensity. Serial P_K analysis demonstrates de-sensitization in skin incision, but not in laparoscopic trocar insertion. Quantitative indices present the transient bradycardia introduced by noxious stimulation. The results indicate different effects between skin incision and trocar insertion.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Background and purpose

With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.

Methods

In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.

Results

As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.

Conclusion

The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.