Monitoring fetal heart rate during pregnancy is essential to assist clinicians in making more timely decisions. Non-invasive monitoring of fetal heart activities using abdominal ECGs is useful for diagnosis of heart defects. However, the extracted fetal ECGs are usually too weak to be robustly detected. Thus, it is a necessity to enhance fetal R-peak since their peaks may hidden within the signal due to the immaturity of the fetal cardiovascular system. Therefore, to improve the detection of the fetal heartbeat, a novel fetal R-peak enhancement technique was proposed to statistically generate the weighting mask according to the distribution of the neighboring temporal intervals between each pair of peaks. Two set of simulations were designed to validate the reliability of the method: challenges with different levels of (1) noise contamination and (2) R-peak interval changing rate. The simulation results showed that the weighting mask improve the accuracy of the R-peak detection rate by 25% and decrease false alarm rate by 20% with white noise contamination, and ensure high R-peak detection rate (> 80%), especially with mild noise contamination (noise amplitude ratio < 1.5 and noise rate per minute < 25%). For those simulations with continuous R-peak intervals changing, the masking process can still effectively eliminate noise contamination especially when the amplitude of the sinusoidal fetal R-R intervals lower than 50msec. For the real fetus ECGs, detection rate was increasing by 3.498% whereas false alarm rate was decreasing by 3.933%. Next, we implement the fetal R-peak enhancement technique to investigate fractal regulation and multiscale entropy of the real fetal heartbeat intervals. Both scaling exponent (∼0.6 to ∼1 in scale 4-15) and entropy measure (scale 6-10) increase from with gestational ages (22-40 weeks). The results confirmed fractal slope and complexity of fetal heartbeat intervals can reflect the maturation of fetus organism.

Background: Among cardiac arrest (CA) survivors, whether the combination of duration of cardiopulmonary resuscitation (CPR) and shockable/nonshockable rhythms during resuscitation can help predict the benefit of targeted temperature management (TTM) remains un-investigated. Materials and methods: This multicenter retrospective cohort study enrolled 479 nontraumatic adult CA survivors with TTM and CPR duration < 60 min during January 2014 to June 2019 from the Taiwan network of targeted temperature ManagEment for CARDiac arrest (TIMECARD) registry. The differences of CPR duration between shockable and nonshockable rhythms in predicting outcomes in the studied population was evaluated. Results: We observed that 205 patients (42.8%) survived to hospital discharge and 100 patients (20.9%) presented favorable neurological outcomes at discharge. The enrolled patients were further re-classified into four groups according to shockable/nonshockable rhythms and CPR duration. Patients with shockable rhythms and shorter CPR duration had better survival-to-discharge (adjusted odds ratio [OR] = 2.729, 95% confidence interval [CI] = 1.384–5.383, P = 0.004) and neurological recovery (adjusted OR = 9.029, 95%CI = 3.263–24.983, P < 0.001) than did those with nonshockable rhythms and longer CPR duration. Conclusion: The CPR duration for predicting outcomes differs between CA patients with shockable and nonshockable rhythms. The combination of shockable/nonshockable rhythms and CPR duration may help predict the prognosis in CA survivors undergoing TTM.

This study investigated the usefulness of ventilator parameters in the prediction of development and outcome of acute respiratory distress syndrome (ARDS) in postoperative patients with esophageal or lung cancer on admission to the surgical intensive care unit (SICU). A total of 32 post-operative patients with lung or esophageal cancer from SICU in a tertiary medical center were retrospectively analyzed. The study patients were divided into an ARDS group (n = 21) and a non-ARDS group (n = 11). The ARDS group contained the postoperative patients who developed ARDS after lung or esophageal cancer surgery. The ventilator variables were analyzed in this study. Principal component analysis (PCA) was performed to reduce the correlated ventilator variables to a small set of variables. The top three ventilator variables with large coefficients, as determined by PCA, were considered as sensitive variables and included in the analysis model based on the rule of 10 events per variable. Firth logistic regression with selective stepwise elimination procedure was performed to identify the most important predictors of morbidity and mortality in patients with ARDS. Ventilator parameters, including rapid shallow breath index during mechanical ventilation (RSBIv), rate pressure product of ventilation (RPPv), rate pressure volume index (RPVI), mechanical work (MW), and inspiration to expiration time ratio (IER), were analyzed in this study. It was found that the ARDS patients had significantly greater respiratory rate (RR), airway resistance (Raw), RSBIv, RPPv, RPVI, positive end-expiratory pressure (PEEP), and IER and significantly lower respiratory interval (RI), expiration time (Te), flow rate ($\dot{\mathrm{V}}$), tidal volume (V_T), dynamic compliance (Cdyn), mechanical work of ventilation (MW), and MW/IER ratio than the non-ARDS patients. The non-survivors of ARDS had significantly greater peak inspiratory pressure above PEEP (PIP), RSBIv, RPPv, and RPVI than the survivors of ARDS. By using PCA, the MW/IER was found to be the most important predictor of the development of ARDS, and both RPPv and RPVI were significant predictors of mortality in patients with ARDS. In conclusion, some ventilator parameters, such as RPPv, RPVI, and MW/IER defined in this study, can be derived from ventilator readings and used to predict the development and outcome of ARDS in mechanically ventilated patients on admission to the SICU.

Respiration is known to be a significant modulator of heart rate, and the high-frequency component in the power spectrum of heart rate variability (HRV) is believed to be caused mainly by respiration. To investigate the effect of respiration on heart rate, cross-spectral analysis of electrocardiographic (ECG) and nostril airflow signals was performed in healthy subjects to find the common features of ECG and respiration. Forty-two healthy subjects were included in this study. The autospectra of respective ECG and nostril airflow signals and the cross-spectra of ECG and nostril airflow signals were obtained and compared with the corresponding conventional HRV measures. We found that there were two spectral peaks at around 0.03 Hz and 0.3 Hz in the autospectrum of nostril airflow and the cross-spectrum of ECG and nostril airflow. In addition, the cross-spectral normalized high-frequency power (nHFPcs) was significantly larger than that of conventional HRV, while the cross-spectral normalized very low-frequency power (nVLFPcs), normalized low-frequency power (nLFPcs), and low-/high-frequency power ratio (LHRcs) were significantly lower than those of the conventional HRV. The cross-spectral nLFPcs and LHRcs had positive correlations with their corresponding HRV measures. We conclude that cross-spectral analysis of ECG and nostril airflow signals identifies two respiratory frequencies at around 0.03 Hz and below and around 0.3 Hz and can yield significantly enhanced nHFPcs and significantly suppressed nVLFPcs, as compared to their counterparts in conventional HRV. Both very low-frequency and high-frequency components of HRV are caused in part or mainly by respiration.

Post-cardiac arrest syndrome (PCAS) remains a major cause of mortality and neurological impairment following successful resuscitation, yet the mechanisms linking global ischemia–reperfusion injury to microvascular and systemic dysfunction are not yet completely understood. While prior work has focused on inflammation, endothelial injury, and circulatory collapse, the central role of platelets in coordinating these pathological processes has not been comprehensively examined. This review provides the first integrated framework positioning platelets as core modulators, rather than secondary participants, in PCAS pathophysiology. We synthesize emerging evidence demonstrating that ischemia and reperfusion transform platelets into potent thromboinflammatory effectors through oxidative stress, DAMP-mediated pattern recognition signaling, and mitochondrial dysfunction. Hyperactivated platelets drive cerebral microthrombus formation, coronary no-reflow, and peripheral organ hypoperfusion, while platelet–leukocyte aggregates, neutrophil extracellular traps, and platelet-derived microparticles amplify systemic inflammation and endothelial injury. We further highlight the clinical significance of dynamic platelet dysfunction in coagulopathy, prognostication, and responses to post-arrest therapies including targeted temperature management and ECMO. Finally, we outline a novel, platelet-centered therapeutic paradigm, emphasizing selective interventions, such as GPVI inhibition, P-selectin blockade, FXI/XIa inhibition, and NETosis modulation, that target pathological platelet activity while preserving essential hemostatic function. In this review, by reframing platelets as the central determinants of PCAS, we report new mechanistic insights and therapeutic opportunities that are complementary to the existing post-arrest strategies and have the potential to improve survival and neurological outcomes after cardiac arrest.