Respiratory Waveform Research Articles

An Improved Vital Signal Extraction Method Based on Laser Doppler Effect.

The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. To filter the IF interference signals and the noise scatters in the time-frequency graph, the filtering method based on coefficient of variation (CoV) values and the enhanced curve extraction method based on noise-scatter theory are utilized in vital signal analysis. To decouple the respiratory signal and the heartbeat signal in time domain, the waveform-fixing method based on second-order difference theory is utilized in signal decoupling. This method as an algorithm is applied in the computer simulation and laboratory environments. The results show that the above methods can extract the mixed waveforms and identify the respiratory rates and heart rates in real experimental data. The IF interference signal can be filtered adaptively, and the accuracy of the analyzed rates can be improved to about 95%.

Shuffled ECA-Net for stress detection from multimodal wearable sensor data

BackgroundRecently, stress has been recognized as a key factor in the emergence of individual and social issues. Numerous attempts have been made to develop sensor-augmented psychological stress detection techniques, although existing methods are often impractical or overly subjective. To overcome these limitations, we acquired a dataset utilizing both wireless wearable multimodal sensors and salivary cortisol tests for supervised learning. We also developed a novel deep neural network (DNN) model that maximizes the benefits of sensor fusion. MethodWe devised a DNN involving a shuffled efficient channel attention (ECA) module called a shuffled ECA-Net, which achieves advanced feature-level sensor fusion by considering inter-modality relationships. Through an experiment involving salivary cortisol tests on 26 participants, we acquired multiple bio-signals including electrocardiograms, respiratory waveforms, and electrogastrograms in both relaxed and stressed mental states. A training dataset was generated from the obtained data. Using the dataset, our proposed model was optimized and evaluated ten times through five-fold cross-validation, while varying a random seed. ResultsOur proposed model achieved acceptable performance in stress detection, showing 0.916 accuracy, 0.917 sensitivity, 0.916 specificity, 0.914 F1-score, and 0.964 area under the receiver operating characteristic curve (AUROC). Furthermore, we demonstrated that combining multiple bio-signals with a shuffled ECA module can more accurately detect psychological stress. ConclusionsWe believe that our proposed model, coupled with the evidence for the viability of multimodal sensor fusion and a shuffled ECA-Net, would significantly contribute to the resolution of stress-related issues.

Breathing Monitoring in Soccer: Part I-Validity of Commercial Wearable Sensors.

Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring fR during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech® (CT) vest or a BioharnessTM (BH) 3.0 strap and a Tyme WearTM (TW) vest. fR was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The fR time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure fR in soccer.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Background and objectivePatient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. MethodsHysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. ResultsSelf-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. ConclusionThe collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

Free-breathing motion corrected magnetic resonance elastography of the abdomen.

Magnetic resonance elastography (MRE) is a non-invasive method to measure the viscoelastic properties of tissue and has been applied in multiple abdominal organs. However, abdominal MRE suffers from detrimental breathing motion causing misalignment of structures between repeated acquisitions for different MRE dimensions (e.g., motion encoding directions and wave phase offsets). This study investigated motion correction strategies to resolve all breathing motion on sagittal free-breathing MRE acquisitions in a phantom, in healthy volunteers and showed feasibility in patients. First, in silico experiments were performed on a static phantom dataset with simulated motion. Second, eight healthy volunteers underwent two sagittal MRE acquisitions in the pancreas and right kidney. The multi-frequency free-breathing spin-echo echo-planar-imaging (SE-EPI) MRE consisted of four frequencies (30, 40, 50, 60 Hz), eight wave-phase offsets, with 3 mm3 isotropic voxel size. Following data re-sorting in different number of motion states (4 till 12) based on respiratory waveform signal, three intensity-based registration methods (monomodal, multimodal, and phase correlation) and non-rigid local registration were compared. A ranking method was used to determine the best registration method, based on seven signal-to-noise and image quality measures. Repeatability was assessed for no motion correction (Original) and the best performing method (Best) using Bland-Altman analysis. Lastly, the best motion correction method was compared to no motion correction on patient MRE data [pancreatic ductal adenocarcinoma (PDAC, n=5) and metabolic dysfunction-associated steatotic liver disease (MASLD) (n=1)]. In silico experiments showed a deviation of shear wave speed (SWS) with simulated motion to the ground truth, which was (partially) resolved using motion correction. In healthy volunteers ranking resulted in the best motion correction method of monomodal registration using nine motion states, while no motion correction was ranked last. Limits of agreement were (-0.18, 0.14), and (-0.25, 0.18) m/s for Best and Original, respectively. Using motion correction in patients resulted in a significant increase in SWS in the pancreas (Original: 1.39±0.10 and Best: 1.50±0.17 m/s). After motion correction PDAC had a mean SWS of 1.56±0.27 m/s (Original: 1.42±0.25 m/s). The fibrotic liver mean SWS was 2.07±0.20 m/s (Original: 2.12±0.18 m/s). Motion correction in sagittal free-breathing abdominal MRE results in improved data quality, inversion precision, repeatability, and is feasible in patients.

Breath-by-breath Analysis of a Rat Model of Pompe Disease Exposes Deficits in High-frequency Behaviors

Pompe disease (PD) is caused by a loss of function of the enzyme acid-α-glucosidase (GAA) leading to glycogen accumulation, neuromuscular dysfunction, and breathing failure. Here, we characterized longitudinal changes in breathing of GAA null ( Gaa−/−) rats on a breath-by-breath basis using a novel respiratory event detection algorithm. Adult Pompe (n = 5) and Sprague Dawley (n = 5) rats were implanted with chronic diaphragm EMG electrodes. Once a month from 4-10 months of age, full body plethysmography with concurrent EMG was used to record respiratory waveforms under room air and hypoxia. Waveforms were analyzed by the Adjustable Baselines Respiratory Analysis Program (ABRAP) algorithm. An adaptive threshold identified “respiratory events” defined as crossings of a threshold set between the recent maxima and minima of the waveform. After event identification, a range of waveform characteristics are calculated and tagged to that event. Averages of each characteristic were taken for all events and then analyzed based on respiratory rate in breaths/minute (bpm): low (<120 bpm, ‘quiet breathing’), medium (between 120 and 240 bpm) and high (>240 bpm, ‘high frequency sniffng’). In room air, Pompe rats had a lower breathing rate than wildtype rats by month 10 (173 vs 285 bpm, standard error of the difference (SE of diff.) 37, p=0.03). This decrease in frequency was driven by the high rate events as there were no differences in the mean rate of low rate events at month 10 (80 vs 83 bpm, SE of diff. 3) while the frequency of high rate events was significantly lower by 9 months (mo 9: 393 vs 440 bpm, SE of diff. 16, p=0.04, mo 10: 383 vs 440 bpm, SE of diff. 17, p=0.008). Not only was the breathing frequency of high-rate events impaired in Pompe rats, but there was a trend towards decrease in the proportion of events falling in the high rate category indicating less time spent in high rate breathing behaviors (mo 4: 52 vs 59%, SE of diff. 6, mo 10: 25 vs 52%, SE of diff. 9) with a concomitant increase in the proportion of low frequency events. Additionally, there was a main effect of genotype on the latency between diaphragm activation and onset of inspiratory flow during hypoxia with Pompe rats having a longer latency across all months and frequency bands. This difference was most pronounced in the medium (44 vs 29 ms, SE of diff. 4 ms, p=0.003) frequency events. Active expiration was impaired in Pompe animals with peak post-event airflow (maximum positive pressure reached before the next inspiratory effort) blunted across all months. This effect was pronounced in the high frequency events (0.16 mL vs 0.24 mL, SE of diff. 0.01, p=0.0001). These deficits in high frequency events are consistent with progressive neuromuscular diaphragm weakness. We conclude that comprehensive analyses of all respiratory events over extended recording periods enables detection of altered breathing behaviors that may be missed when assessing only short periods of quiet breathing. In turn, this may help uncover a respiratory signature of disease progression in Pompe disease or other neuromuscular conditions. 2R01HD052682-11A1 (DDF, BJB), T32HL134621 (AM), R01HL153102 (ED), SPARC OT2OD023854 (ED), Craig H. Neilsen Pilot Grant (ED). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Feasibility of a portable respiratory training system with a gyroscope sensor.

A portable respiratory training system with a gyroscope sensor (gyroscope respiratory training system [GRTS]) was developed and the feasibility of respiratory training was evaluated. Simulated respiratory waveforms from a respiratory motion phantom and actual respirator waveforms from volunteers were acquired using the GRTS and Respiratory Gating for Scanners system (RGSC). Respiratory training was evaluated by comparing the stability and reproducibility of respiratory waveforms from patients undergoing expiratory breath-hold radiation therapy, with and without the GRTS. The stability and reproducibility of respiratory waveforms were assessed by root mean square error and gold marker placement-based success rate of expiratory breath-hold, respectively. The absolute mean difference for sinusoidal waveforms between the GRTS and RGSC was 2.0%. Among volunteers, the mean percentages of errors within ±15% of the respiratory waveforms acquired by the GRTS and RGSC were 96.1% for free breathing and 88.2% for expiratory breath-hold. The mean root mean square error and success rate of expiratory breath-hold (standard deviation) with and without the GRTS were 0.65 (0.24) and 0.88 (0.89) cm and 91.0% (6.9) and 89.1% (11.6), respectively. Respiratory waveforms acquired by the GRTS exhibit good agreement with waveforms acquired by the RGSC. Respiratory training with the GRTS reduces inter-patient variability in respiratory waveforms, thereby improving the success of expiratory breath-hold radiation therapy. A respiratory training system with a gyroscope sensor is inexpensive and portable, making it ideal for respiratory training. This is the first report concerning clinical implementation of a respiratory training system.

Design of Pulse and Respiratory Signals Detection System

A pulse and respiration synchronous detection system is designed to explore the changes of physiological signals in different situations. The system obtains the corresponding signal through STM32 control pulse and respiratory acquisition circuit, calculates and displays real-time parameters such as heart rate and respiratory rate, and transmits the data to the upper computer for storage in the database. The experimental test results show that the system can monitor pulse and respiratory waveform in different situations, and the waveform is in good condition. Compared with medical pulse oximeter, the error of measured heart rate and blood oxygen concentration is less than 3%, and the error of respiratory rate is less than 5% compared with the actual value, which verifies the accuracy of system signal acquisition. The system is small in size, low in cost, and comfortable to wear, and can be applied in experimental research related to pulse and respiratory signals.

Early Physiologic Numerical and Waveform Characteristics of Simulated Hemorrhagic Events With Healthy Volunteers Donating Blood.

Early signs of bleeding are often masked by the physiologic compensatory responses delaying its identification. We sought to describe early physiologic signatures of bleeding during the blood donation process. Waveform-level vital sign data including electrocardiography, photoplethysmography (PPG), continuous noninvasive arterial pressure, and respiratory waveforms were collected before, during, and after bleeding. Fifty-five healthy volunteers visited blood donation center to donate whole blood. After obtaining the informed consent, 3 minutes of resting time was given to each subject. Then 3 minutes of orthostasis was done, followed by another 3 minutes of resting before the blood donation. After the completion of donating blood, another 3 minutes of postbleeding resting time, followed by 3 minutes of orthostasis period again. From 55 subjects, waveform signals as well as numerical vital signs (heart rate [HR], respiratory rate, blood pressure) and clinical characteristics were collected, and data from 51 subjects were analyzable. Any adverse events (AEs; dizziness, lightheadedness, nausea) were documented. Statistical and physiologic features including HR variability (HRV) metrics and other waveform morphologic parameters were modeled. Feature trends for all participants across the study protocol were analyzed. No significant changes in HR, blood pressure, or estimated cardiac output were seen during bleeding. Both orthostatic challenges and bleeding significantly decreased time domain and high-frequency domain HRV, and PPG amplitude, whereas increasing PPG amplitude variation. During bleeding, time-domain HRV feature trends were most sensitive to the first 100 mL of blood loss, and incremental changes of different HRV parameters (from 300 mL of blood loss), as well as a PPG morphologic feature (from 400 mL of blood loss), were shown with statistical significance. The AE group (n = 6) showed decreased sample entropy compared with the non-AE group during postbleed orthostatic challenge (p = 0.003). No significant other trend differences were observed during bleeding between AE and non-AE groups. Various HRV-related features were changed during rapid bleeding seen within the first minute. Subjects with AE during postbleeding orthostasis showed decreased sample entropy. These findings could be leveraged toward earlier identification of donors at risk for AE, and more broadly building a data-driven hemorrhage model for the early treatment of critical bleeding.

Waveform characteristics in thoracic paravertebral space: a prospective observational study

Background With increased use of thoracic paravertebral block (TPVB) in thoracic surgery, many faced the challenge of locating the thoracic paravertebral space (TPVS) ultrasonographically. This observational study aimed to investigate the waveform characteristics and pressure value within the TPVS in anaesthetized patients with controlled ventilation. Methods 50 patients scheduled for elective lung surgery were enrolled. After conduction of anesthesia, all patients underwent TPVB at T4/5 and T6/7 using transverse, in-plane ultrasound guidance. A pressure transducer system with a desktop monitor was connected to the needle hub to measure pressure values and waveform characteristics in three locations: the paraspinal muscles, immediately behind the superior costotransverse ligament, and within the TPVS. Next, 15 mL of 0.33% bupivacaine was injected into each desired TPVS. After completion of the surgery, the extent of dermatomal blockade and the pain score was assessed in all patients. Results 98 typical regular respiratory waveforms with a mean pressure of ≤ 25 mmHg were detected in the TPVS of 50 patients. The sensitivity of the combined ultrasound and pressure waveform measurement technique to identify the TPVS was 95.45% (95% confidence interval, 84.527–99.445). Nontypical respiratory waveforms were present in two patients. Factors interfering with the TPVS waveform characteristics were previous thoracic surgery and chronic pleural inflammation. Conclusion The TPVS had low pressure and showed a smooth, regular waveform pattern corresponding to respiration.

Contactless Respiration Monitoring Using Wi-Fi and Artificial Neural Network Detection Method.

Detecting respiration in a non-intrusive manner is beneficial not only for convenience but also for cases where the traditional ways cannot be applied. This paper presents a novel simple low-cost system where ambient Wi-Fi signals are acquired by a third-party tool (Nexmon) installed in a Raspberry Pi and is able to detect the respiration time domain waveform of a person. This tool was selected as it uses 80 MHz bandwidth of the Wi-Fi signal and supports the latest implementations that are widely used, such as 802.11ac. A neural network is developed to detect the respiration frequency of the waveform. Generated waves emulating respiration waveforms were used for training, validating, and testing the model. The model can be applied to unseen real measurement data and successfully determine the breathing frequency with a very low average error of 4.7% tested in 20 measurement datasets.

A high altitude respiration and SpO2 dataset for assessing the human response to hypoxia

This report presents the Harespod dataset, an open dataset for high altitude hypoxia research, which includes respiration and SpO2 data. The dataset was collected from 15 college students aged 23–31 in a hypobaric oxygen chamber, during simulated altitude changes and induced hypoxia. Real-time physiological data, such as oxygen saturation waveforms, oxygen saturation, respiratory waveforms, heart rate, and pulse rate, were obtained at 100 Hz. Approximately 12 hours of valid data were collected from all participants. Researchers can easily identify the altitude corresponding to physiological signals based on their inherent patterns. Time markers were also recorded during altitude changes to facilitate realistic annotation of physiological signals and analysis of time-difference-of-arrival between various physiological signals for the same altitude change event. In high altitude scenarios, this dataset can be used to enhance the detection of human hypoxia states, predict respiratory waveforms, and develop related hardware devices. It will serve as a valuable and standardized resource for researchers in the field of high altitude hypoxia research, enabling comprehensive analysis and comparison.

CVRanalysis: a free software for analyzing cardiac, vascular and respiratory interactions.

Introduction: Simultaneous beat-to-beat R-R intervals, blood pressure and respiration signals are routinely analyzed for the evaluation of autonomic cardiovascular and cardiorespiratory regulations for research or clinical purposes. The more recognized analyses are i) heart rate variability and cardiac coherence, which provides an evaluation of autonomic nervous system activity and more particularly parasympathetic and sympathetic autonomic arms; ii) blood pressure variability which is mainly linked to sympathetic modulation and myogenic vascular function; iii) baroreflex sensitivity; iv) time-frequency analyses to identify fast modifications of autonomic activity; and more recently, v) time and frequency domain Granger causality analyses were introduced for assessing bidirectional causal links between each considered signal, thus allowing the scrutiny of many physiological regulatory mechanisms. Methods: These analyses are commonly applied in various populations and conditions, including mortality and morbidity predictions, cardiac and respiratory rehabilitation, training and overtraining, diabetes, autonomic status of newborns, anesthesia, or neurophysiological studies. Results: We developed CVRanalysis, a free software to analyze cardiac, vascular and respiratory interactions, with a friendly graphical interface designed to meet laboratory requirements. The main strength of CVRanalysis resides in its wide scope of applications: recordings can arise from beat-to-beat preprocessed data (R-R, systolic, diastolic and mean blood pressure, respiration) or raw data (ECG, continuous blood pressure and respiratory waveforms). It has several tools for beat detection and correction, as well as setting of specific areas or events. In addition to the wide possibility of analyses cited above, the interface is also designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in text files that are easily employable in statistical softwares. Conclusion: CVRanalysis is freely available at this website: anslabtools.univ-st-etienne.fr. It has been developed using MATLAB® and works on Windows 64-bit operating systems. The software is a standalone application avoiding to have programming skills and to install MATLAB. The aims of this paper area are to describe the physiological, research and clinical contexts of CVRanalysis, to introduce the methodological approach of the different techniques used, and to show an overview of the software with the aid of screenshots.

Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques

Respiratory diseases require early diagnosis and continuous monitoring, but existing methods involve risky physical contact. This study proposes a new system that uses FMCW radar and machine learning to monitor breathing without contact. FMCW radar can detect respiratory movements in real-time, while machine learning can classify respiratory waveforms. This study evaluates the system with cross-validation Shuffle Split, K-fold, and Stratified K-fold. The results show that Random Forest has the highest accuracy of 94.6% and Naïve Bayes has the shortest time of 0.055 seconds. Shuffle Split performs best overall. This study shows the feasibility and potential of the system for the detection, response, and tracking of respiratory diseases in emergencies.

Evaluation of a New Method for CyberKnife Treatment for Central Lung and Mediastinal Tumors by Tracheobronchial Tracking.

CyberKnife treatment for central lung tumors and mediastinal tumors can be difficult to perform with marker less. We aimed to evaluate a novel tracheobronchial-based method (ie, tracheobronchial tracking) for the purpose of minimally invasive CyberKnife treatment for central lung and mediastinal tumors. Five verification plans were created using an in-house phantom. Each plan included five irradiation sessions. The reference plan irradiated and tracked the simulated tumor (using the target tracking volume, TTV). Trachea plans tracked the simulated tracheo-bronchus and irradiated the simulated tumor and included two types of subplans: correlated plans in which the displacement of the simulated tracheobronchial and the simulated tumor were correlated, and non-correlated plans in which these factors were not correlated. Moreover, 15 mm and 25 mm TTVs were evaluated for each plan. The sin waveform and the patient's respiratory waveform were prepared as the respiratory model. Evaluations were performed by calculating the dose difference between the radiophotoluminescent glass dosimeter (RPLD)-generated mean dose values (generated by the treatment planning system, TPS) and the actual absorbed RPLD dose. Statistical analyses were performed to evaluate findings for each plan. Correlation and prediction errors were calculated for each axis of each plan using log files to evaluate tracking accuracy. Dose differences were statistically significant only in comparisons with the non-correlated plan. When evaluated using the sin waveform, the mean values for correlation and prediction errors in each axis and for all plans were less than 0.6 mm and 0.1 mm, respectively. In the same manner, they were less than 1.1 mm and 0.2 mm when evaluated using the patient's respiratory waveform. Our newly-developed tracheobronchial tracking method would be useful in facilitating minimally invasive CyberKnife treatment in certain cases of central lung and mediastinal tumors.

Optimal dose of vigorous physical activity on cardiorespiratory and perceptual response for sedentary youths using internal load monitoring.

Vigorous physical activity (VPA) has been demonstrated to enhance cardiorespiratory fitness (CRF) in sedentary college students more effectively than other PA. However, differences in training volume may affect this outcome. This study examines the physiological, psychological, and internal training load (ITL) characteristics of VPA with varying volumes in a single session. Thirty sedentary college students were divided into three groups: high-intensity interval training (HIIT), sprint interval training (SIT), and threshold training (THR). PA process was monitored. The study measured various cardiorespiratory parameters, including heart rate (HR), respiratory waveform and amplitude, respiratory rate (RR), tidal volume (TV), minute ventilation volume (VE), fractional concentration of oxygen in end-tidal gas (O2%), fractional concentration of end-tidal carbon dioxide (CO2%), global oxygen consumption (VO2), carbon dioxide discharge (VCO2), and the amount of carbon dioxide in the air. The following physiological indicators were measured: carbon dioxide discharge (VCO2), Oxygen pulse (OP), and respiratory exchange ratio (RER). Additionally, subjective perception indicators were recorded, including the feeling scale (FS), rating of perceived exertion (RPE), and dual-mode model (DMM). The session-RPE (s-RPE) and Edward's TRIMP were used to measure ITL. There were no significant differences in HR across the three conditions. THR had the highest level of TV (p = 0.043), but RR was significantly lower than that of HIIT and SIT (p < 0.01). HIIT had the highest levels of VO2, VCO2, O2%, and OP (p < 0.05). RPE was higher in HIIT and SIT compared to THR (p < 0.01), but the difference in FS was not significant. The DMM time-domain trajectories were similar in HIIT and THR. The correlation between exercise intensity, RPE, and FS was highest in THR group (r = 0.453, r = -0.58, r = -0.885). ITL did not show a significant difference between three conditions, but TRIMP and s-RPE readings were opposite in magnitude. This study proposes that using an appropriate amount of THR to foster interest and adaptive strength during the PA habit establishment period, incorporating HIIT to enhance exercise efficiency during the adaptation period, and implementing SIT to reduce the monotony may effectively enhance the cardiorespiratory fitness of sedentary college students and establish PA habit.

Esophageal balloon catheter system identification to improve respiratory effort time features and amplitude determination

Objective. Understanding a patient’s respiratory effort and mechanics is essential for the provision of individualized care during mechanical ventilation. However, measurement of transpulmonary pressure (the difference between airway and pleural pressures) is not easily performed in practice. While airway pressures are available on most mechanical ventilators, pleural pressures are measured indirectly by an esophageal balloon catheter. In many cases, esophageal pressure readings take other phenomena into account and are not a reliable measure of pleural pressure. Approach. A system identification approach was applied to provide accurate pleural measures from esophageal pressure readings. First, we used a closed pressurized chamber to stimulate an esophageal balloon and model its dynamics. Second, we created a simplified version of an artificial lung and tried the model with different ventilation configurations. For validation, data from 11 patients (five male and six female) were used to estimate respiratory effort profile and patient mechanics. Main results. After correcting the dynamic response of the balloon catheter, the estimates of resistance and compliance and the corresponding respiratory effort waveform were improved when compared with the adjusted quantities in the test bench. The performance of the estimated model was evaluated using the respiratory pause/occlusion maneuver, demonstrating improved agreement between the airway and esophageal pressure waveforms when using the normalized mean squared error metric. Using the corrected muscle pressure waveform, we detected start and peak times 130 ± 50 ms earlier and a peak amplitude 2.04 ± 1.46 cmH2O higher than the corresponding estimates from esophageal catheter readings. Significance. Compensating the acquired measurements with system identification techniques makes the readings more accurate, possibly better portraying the patient’s situation for individualization of ventilation therapy.

Rib cage contributions to inspiratory capacity in patients with cervical spinal cord injury

BackgroundCervical spinal cord injury (CSI) often leads to impaired respiratory function, affecting the overall well-being of patients. This study aimed to investigate the influence of rib cage motion on inspiratory capacity in CSI patients. MethodsWe conducted a study with 11 CSI patients, utilising respiratory inductance plethysmography (RIP). We measured ventilatory volume by spirometry concurrently with RIP. Participants were instructed to perform maximal inspiratory efforts. Inspiratory capacity (IC) was calculated from spirometry waveforms. We converted the respiratory waveforms of the chest and abdomen into inspiratory volume measured by a spirometer. The inspiratory volume measured by the chest sensor was defined as VRIP-rib cage (VRIP-rc), and the inspiratory volume measured by the abdominal sensor was defined as VRIP-abdomen (VRIP-ab). Subsequently, the relationships of IC with VRIP-rc and VRIPab were assessed. ResultsThe mean IC was 1.828 ± 0.459 L, with the mean VRIP-rc at 1.343 ± 0.568 L and the mean VRIP-ab at 0.485 ± 0.427 L. A significant correlation was observed between IC and VRIP-rc (r = 0.67, p = 0.02), indicating that rib cage motion significantly influences IC in CSI patients. ConclusionThis study highlights the importance of rib cage motion in assessing inspiratory capacity in patients with CSI.

Noncontact Cardiac Activity Detection Based on Single-Channel ISM Band FMCW Radar.

The heart is an important organ that maintains human life activities, and its movement reflects its health status. Utilizing electromagnetic waves as a sensing tool, radar sensors enable noncontact measurement of cardiac motion, offering advantages over conventional contact-based methods in terms of comfort, hygiene, and efficiency. In this study, the high-precision displacement detection algorithm of radar is applied to measure cardiac motion. Experimental is conducted using a single out-channel frequency modulated continuous wave (FMCW) radar operating in the ISM frequency band with a center frequency of 24 GHz and a bandwidth of 150 MHz. Since the detection signal is influenced by both respiratory and heartbeat movements, it is necessary to eliminate the respiratory signal from the measurement signal. Firstly, the harmonic composition of the respiratory signal is analyzed, and a method is proposed to calculate the parameters of the respiratory waveform by comparing the respiratory waveform coverage area with the area of the circumscribed rectangle. This allows for determining the number of respiratory harmonics, assisting in determining whether respiratory harmonics overlap with the frequency range of the heartbeat signal. Subsequently, a more accurate cardiac motion waveform is extracted. A reference basis is provided for extracting cardiac health information from radar measurement waveforms by analyzing the corresponding relationship between certain extreme points of the waveform and characteristic positions of the electrocardiogram (ECG) signal. This is achieved by eliminating the fundamental frequency component of the heartbeat waveform to emphasize other spectral components present in the heartbeat signal and comparing the heartbeat waveform, the heartbeat waveform with the fundamental frequency removed, and the heartbeat velocity waveform with synchronized ECG signals.

Abstract 12295: The Timing of Appearance of Untimely Dissociated Potentials After Pulmonary Vein Isolation for Atrial Fibrillation is Related to the Respiratory Phase

Introduction: Ectopic beats originating from the pulmonary vein (PV) trigger atrial fibrillation (AF). However, the mechanism and timing of Ectopic beat occurrence remain unclear. Hypothesis: The dissociated potentials (DPs) after pulmonary vein isolation (PVI) for AF are related to the respiratory phase and left atrial pressure (LAP). Methods: 93 patients underwent AF ablation under deep sedation and BiPAP ventilation. [Study1] Respiratory waveforms of 20 patients were recorded simultaneously with LAP. [Study2] In 73 patients, DPs after PVI or left atrial posterior wall (LAPW) isolation were recorded simultaneously with respiratory waveforms for 3 min, and the preceding coupling intervals (CI) of all DPs were measured. Sites with DPs frequencies &lt;10/min were defined as rare DPs sites. In sites with DPs frequencies ≧10/min, sites where the number of DPs with CI shorter than the mean - SD of CI was greater than the number of DPs with CI longer than the mean + SD of CI were defined as short-coupling DPs sites, and long-coupling DPs sites were defined in the opposite cases. Results: [Study1] Respiratory waveforms showed fluctuations similar to LAP, and LAP increased within the respiratory interval in all patients (Fig.1). [Study2] The incidence of DPs on the left PV was lower than that on the right PV and LAPW (28 of 61 left PV 46%, 41 of 66 62%, 3 of 3 100%, P=0.0306). Additionally, the CI of DPs on the left PV was longer (3.1±5.4sec, 2.0±2.8, and 1.6±0.5, P&lt;0.0001). The timing of appearance of DPs was not related to the respiratory phase in all DPs assessments (2126 beats during the respiratory interval and 2155 beats during the rest interval). However, inopportune DPs appeared during the specific respiratory phases: the respiratory interval for short-coupling DPs, and the rest interval for long-coupling DPs and rare DPs sites (P=0.016, Fig.2). Conclusions: The timing of appearance of untimely DPs is related to the respiratory phase and may be associated with an increase in LAP.