A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising.

Esophageal pressure (Peso) is one of the most common and minimally invasive methods used to assess the respiratory and lung mechanics in patients receiving mechanical ventilation. However, the Peso measurement is contaminated by cardiogenic oscillations (CGOs), which cannot be easily eliminated in real-time. The field of study dealing with the elimination of CGO from Peso signals is still in the early stages of its development. In this study, we present an adaptive filtering-based method by constructing a reference signal based on the heart rate and sine function to remove CGOs in real-time. The proposed technique is tested using clinical data acquired from 20 patients admitted to the intensive care unit. Lung compliance ( QUOTE ) and esophageal pressure swings (△Pes) are used to evaluate the performance and efficiency of the proposed technique. The CGO can be efficiently suppressed when the constructional reference signal contains the fundamental, and second and third harmonic frequencies of the heart rate signal. The analysis of the data of 8 patients with controlled mechanical ventilation reveals that the standard deviation/mean of the QUOTE is reduced by 28.4-79.2% without changing the QUOTE and the △Pes measurement is more accurate, with the use of our proposed technique. The proposed technique can effectively eliminate the CGOs from the measured Peso signals in real-time without requiring additional equipment to collect the reference signal.

Ventilator auto-cycling from cardiogenic oscillations: case report and review of literature.

BACKGROUND: Brain death is the total loss of all brain and brain stem functions, and its diagnosis is often confirmed by an apnoea test, which relies on disconnecting the patient from the ventilator. Auto-triggering or auto-cycling is defined as a ventilator being triggered in the absence of patient effort, intrinsic respiratory drive or inspiratory muscle activity. Ventilator auto-triggering could delay the diagnosis of brain death leading to unnecessary admission for the patient and false hopes of recovery for the family. METHODS: We report a case of ventilator auto-triggering associated with cardiogenic oscillations in a female patient. RESULTS: We confirmed the finding of ventilator auto-triggering by changing the patient's position and reassessing the triggering thresholds. Brain death was then confirmed by apnoea test. CONCLUSION: This case is presented to arouse the awareness of the medical staff and nurses to this phenomenon, which can mimic an intrinsic respiratory effort in patients allegedly diagnosed with brain death. Along with this case report, we review the English language publications for similar cases.

Extraction of cardiac-related signals from a suprasternal pressure sensor during sleep.

Objective.The accurate detection of respiratory effort during polysomnography is a critical element in the diagnosis of sleep-disordered breathing conditions such as sleep apnea. Unfortunately, the sensors currently used to estimate respiratory effort are either indirect and ignore upper airway dynamics or are too obtrusive for patients. One promising alternative is the suprasternal notch pressure (SSP) sensor: a small element placed on the skin in the notch above the sternum within an airtight capsule that detects pressure swings in the trachea. Besides providing information on respiratory effort, the sensor is sensitive to small cardiac oscillations caused by pressure perturbations in the carotid arteries or the trachea. While current clinical research considers these as redundant noise, they may contain physiologically relevant information.Approach.We propose a method to separate the signal generated by cardiac activity from the one caused by breathing activity. Using only information available from the SSP sensor, we estimate the heart rate and track its variations, then use a set of tuned filters to process the original signal in the frequency domain and reconstruct the cardiac signal. We also include an overview of the technical and physiological factors that may affect the quality of heart rate estimation. The output of our method is then used as a reference to remove the cardiac signal from the original SSP pressure signal, to also optimize the assessment of respiratory activity. We provide a qualitative comparison against methods based on filters with fixed frequency cutoffs.Main results.In comparison with electrocardiography (ECG)-derived heart rate, we achieve an agreement error of 0.06 ± 5.09 bpm, with minimal bias drift across the measurement range, and only 6.36% of the estimates larger than 10 bpm.Significance.Together with qualitative improvements in the characterization of respiratory effort, this opens the development of novel portable clinical devices for the detection and assessment of sleep disordered breathing.

Cardiogenic Auto-Triggering as a Consequence of Hemoperitoneum.

A 70-year-old woman presented with hemorrhagic shock secondary to hemoperitoneum following a paracentesis. On hospital day 3, she developed respiratory alkalosis and increased respiratory rates observed on the ventilator despite no spontaneous inspiratory effort. Converting to pressure support mode uncovered a cardiogenic oscillatory flow that had been auto-triggering the ventilator. This cardiogenic auto-triggering resolved with large-volume paracentesis. Cardiogenic auto-triggering leads to patient-ventilator dyssynchrony, respiratory alkalosis, lung distension, and difficulty with weaning from the ventilator, and it may be unrecognized in ICUs.

Observation of cardiogenic flow oscillations in healthy subjects with hyperpolarized 3He MRI.

Recently, dynamic MRI of hyperpolarized (3)He during inhalation revealed an alternation of the image intensity between left and right lungs with a cardiac origin (Sun Y, Butler JP, Ferrigno M, Albert MS, Loring SH. Respir Physiol Neurobiol 185: 468-471, 2013). This effect is investigated further using dynamic and phase-contrast flow MRI with inhaled (3)He during slow inhalations (flow rate ∼100 ml/s) to elucidate airflow dynamics in the main lobes in six healthy subjects. The ventilation MR signal and gas inflow in the left lower lobe (LLL) of the lungs were found to oscillate clearly at the cardiac frequency in all subjects, whereas the MR signals in the other parts of the lungs had a similar oscillatory behavior but were smaller in magnitude and in anti-phase to the signal in the left lower lung. The airflow in the main bronchi showed periodic oscillations at the frequency of the cardiac cycle. In four of the subjects, backflows were observed for a short period of time of the cardiac cycle, demonstrating a pendelluft effect at the carina bifurcation between the left and right lungs. Additional (1)H structural MR images of the lung volume and synchronized ECG recording revealed that maximum inspiratory flow rates in the LLL of the lungs occurred during systole when the corresponding left lung volume increased, whereas the opposite effect was observed during diastole, with gas flow redirected to the other parts of the lung. In conclusion, cardiogenic flow oscillations have a significant effect on regional gas flow and distribution within the lungs.

Breathing-phase selective filtering of respiratory data improves analysis of dynamic respiratory mechanics.

BACKGROUND: The analysis of non-linear respiratory system mechanics under the dynamic conditions of controlled mechanical ventilation is affected by systemic disturbances of the respiratory signals. Cardio-pulmonary coupling induces cardiogenic oscillations to the respiratory signals, which appear prominently in the second half of expiration. OBJECTIVE: We hypothesized that breathing phase-selective filtering of expiratory data improves the analysis of respiratory system mechanics. METHODS: We retrospectively analyzed data from a multicenter-study (28 patients with injured lungs, under volume-controlled ventilation) and from two additional studies (3 lung healthy patients and 3 with injured lungs, under pressure-controlled ventilation). Data streams were recorded at different levels of positive end-expiratory pressure. Using the gliding-SLICE method, intratidal dynamic respiratory mechanics were analyzed with and without low-pass filtering of expiratory or inspiratory data separately. The quality of data analysis was derived from the coefficient of determination R^2. RESULTS: Without filtering, R^2 lay below 0.995 for 87 of 280 investigated data streams. In 68 cases expiration-selective low-pass filtering improved the quality of analysis to R^2 ⩾ 0.995. In contrast, inspiration-selective filtering did not improve R^2. CONCLUSIONS: The selective filtering of expiration data eliminates negative side-effects of cardiogenic oscillations thus leading to a significant improvement of the analysis of dynamic respiratory system mechanics.