Analysis Of Respiratory Signals Research Articles

Use of respiratory signal analysis to assess severity of Brachycephalic Obstructive Airway Syndrome (BOAS) in dogs

Brachycephalic Obstructive Airway Syndrome (BOAS) is a potentially life-threatening condition that can be challenging to diagnose and grade objectively. The aim of this study was to investigate the use of respiratory signal analysis to assess severity of BOAS in dogs. Hundred and seventeen client-owned dogs of brachycephalic and non-brachycephalic breeds were enrolled. Respiratory sounds were recorded using an electronic stethoscope before and after a 3-minute exercise test (ET). Dogs were assigned a BOAS severity grade (BOAS 0–3) using a validated respiratory functional grading scheme. Signal analysis techniques were used to identify seven sound variables. Analysis of variance (ANOVA) was used to investigate associations between variables and BOAS severity and receiver operating characteristic (ROC) curves to assess the diagnostic efficacy of each sound variable. For each sound variable, there was a significant association with BOAS grade. An increase in BOAS grade resulted in greater sound magnitude in the frequency spectrum (0–1000 Hz), and in a greater contribution of lower frequencies (170–260 Hz). The variable “Peak 1” had the best performance in predicting BOAS negative (BOAS 0 +1) versus BOAS positive dogs (BOAS 2 + 3) before the ET; area under the curve (AUC) = 76.6 % (95 % confidence interval 67.4–85.8 %), whereas the variable “Valley 1” had the highest predictive value after the ET; AUC = 87.8 % (95 % confidence interval 81.4–94.3 %). Respiratory signal analysis has good potential for assessing BOAS severity and could be valuable for clinicians in clinical decision processes and for breeders when selecting suitable breeding dogs.

Use of Intrinsic Entropy to Assess the Instantaneous Complexity of Thoracoabdominal Movement Patterns to Indicate the Effect of the Iso-Volume Maneuver Trial on the Performance of the Step Test.

The recent surge in interest surrounds the analysis of physiological signals with a non-linear dynamic approach. The measurement of entropy serves as a renowned method for indicating the complexity of a signal. However, there is a dearth of research concerning the non-linear dynamic analysis of respiratory signals. Therefore, this study employs a novel method known as intrinsic entropy (IE) to assess the short-term dynamic changes in thoracoabdominal movement patterns, as measured by respiratory inductance plethysmography (RIP), during various states such as resting, step test, recovery, and iso-volume maneuver (IVM) trials. The findings reveal a decrease in IE of thoracic wall movement (TWM) and an increase in IE of abdominal wall movement (AWM) following the IVM trial. This suggests that AWM may dominate the breathing exercise after the IVM trial. Moreover, due to the high temporal resolution of IE, it proves to be a suitable measure for assessing the complexity of thoracoabdominal movement patterns under non-stationary states such as the step test and recovery. The results also demonstrate that the instantaneous complexity of TWM and AWM can effectively capture instantaneous changes during non-stationary states, which may prove valuable in understanding the respiratory mechanism for healthcare purposes in daily life.

Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt

This paper describes a novel way to measure, process, analyze, and compare respiratory signals acquired by two types of devices: a wearable sensorized belt and a microwave radar-based sensor. Both devices provide breathing rate readouts. First, the background research is presented. Then, the underlying principles and working parameters of the microwave radar-based sensor, a contactless device for monitoring breathing, are described. The breathing rate measurement protocol is then presented, and the proposed algorithm for octave error elimination is introduced. Details are provided about the data processing phase; specifically, the management of signals acquired from two devices with different working principles and how they are resampled with a common processing sample rate. This is followed by an analysis of respiratory signals experimentally acquired by the belt and microwave radar-based sensors. The analysis outcomes were checked using Levene’s test, the Kruskal–Wallis test, and Dunn’s post hoc test. The findings show that the proposed assessment method is statistically stable. The source of variability lies in the person-triggered breathing patterns rather than the working principles of the devices used. Finally, conclusions are derived, and future work is outlined.

Retrospective Camera-Based Respiratory Gating in Clinical Whole-Heart 4D Flow MRI.

BackgroundRespiratory gating is generally recommended in 4D flow MRI of the heart to avoid blurring and motion artifacts. Recently, a novel automated contact‐less camera‐based respiratory motion sensor has been introduced.PurposeTo compare camera‐based respiratory gating (CAM) with liver‐lung‐navigator‐based gating (NAV) and no gating (NO) for whole‐heart 4D flow MRI.Study TypeRetrospective.SubjectsThirty two patients with a spectrum of cardiovascular diseases.Field Strength/SequenceA 3T, 3D‐cine spoiled‐gradient‐echo‐T1‐weighted‐sequence with flow‐encoding in three spatial directions.AssessmentRespiratory phases were derived and compared against each other by cross‐correlation. Three radiologists/cardiologist scored images reconstructed with camera‐based, navigator‐based, and no respiratory gating with a 4‐point Likert scale (qualitative analysis). Quantitative image quality analysis, in form of signal‐to‐noise ratio (SNR) and liver‐lung‐edge (LLE) for sharpness and quantitative flow analysis of the valves were performed semi‐automatically.Statistical TestsOne‐way repeated measured analysis of variance (ANOVA) with Wilks's lambda testing and follow‐up pairwise comparisons. Significance level of P ≤ 0.05. Krippendorff's‐alpha‐test for inter‐rater reliability.ResultsThe respiratory signal analysis revealed that CAM and NAV phases were highly correlated (C = 0.93 ± 0.09, P < 0.01). Image scoring showed poor inter‐rater reliability and no significant differences were observed (P ≥ 0.16). The image quality comparison showed that NAV and CAM were superior to NO with higher SNR (P = 0.02) and smaller LLE (P < 0.01). The quantitative flow analysis showed significant differences between the three respiratory‐gated reconstructions in the tricuspid and pulmonary valves (P ≤ 0.05), but not in the mitral and aortic valves (P > 0.05). Pairwise comparisons showed that reconstructions without respiratory gating were different in flow measurements to either CAM or NAV or both, but no differences were found between CAM and NAV reconstructions.Data ConclusionCamera‐based respiratory gating performed as well as conventional liver‐lung‐navigator‐based respiratory gating. Quantitative image quality analysis showed that both techniques were equivalent and superior to no‐gating‐reconstructions. Quantitative flow analysis revealed local flow differences (tricuspid/pulmonary valves) in images of no‐gating‐reconstructions, but no differences were found between images reconstructed with camera‐based and navigator‐based respiratory gating.Level of Evidence3Technical EfficacyStage 2

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4D CT.

Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632). Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data. Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning. The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.

Sin-quadratic model for chest tomosynthesis respiratory signal analysis and its application in four dimensional chest tomosynthesis reconstruction

Chest tomosynthesis (CTS) is a newly developed imaging technique which provides pseudo-3D volume anatomical information of thorax from limited-angle projections and contains much less of superimposed anatomy than the chest X-ray radiography. One of the relatively common problems in CTS is the patient respiratory motion during image acquisition, which negatively impacts the detectability. In this work, we propose a sin-quadratic model to analyze the respiratory motion during CTS scan, which is a real time method where the respiratory signal is generated by extracting the motion of diaphragm from projection radiographs. According to the estimated respiratory signal, the CTS projections were then amplitude-based sorted into four to eight phases, and an iterative reconstruction strategy with total variation regularization was adopted to reconstruct the CTS images at each phase. Simulated digital XCAT phantom data and three sets of patient data were adopted for the experiments to validate the performance of the sin-quadratic model and its application in four dimensional (4D) CTS reconstruction. Results of the XCAT phantom simulation study show that the correlation coefficient between the extracted respiratory signal and the originally designed respiratory signal is 0.9964, which suggests that the proposed model could exactly extract the respiratory signal from CTS projections. The 4D CTS reconstructions of both the phantom data and the patient data show clear reduction of motion-induced blur.

A Novel Signal Acquisition System for Wearable Respiratory Monitoring

In this paper, a novel human respiration detection method based on angular velocity is proposed for continuous acquisition and analysis of respiratory signals. Using patient-specific information derived from a wearable sensor, our proposed system is capable of monitoring human respiration and assists in identifying potential respiratory disorders by relying on pre-defined simple procedures. Specifically, our signal verification platform is equipped with a carbon dioxide concentration detection device in order to acquire a synchronous signal. In addition, a median filter method is used to extract the respiratory waveform from the original signal. The obtained angular velocity waveform pertaining to human respiration is then compared to the respiratory carbon dioxide concentration waveform, and the validity of our designated parameters is verified. The test results demonstrate that our implemented system is appropriate for unobtrusive respiratory signals acquisition and analysis; therefore, it can be considered a potential alternative for physiological monitoring and respiratory disorders screening.

Correlation analysis of respiratory signals by using parallel coordinate plots

Background and objectivesThe understanding of the bonds and the relationships between the respiratory signals, i.e. the airflow, the mouth pressure, the relative temperature and the relative humidity during breathing may provide the improvement on the measurement methods of respiratory mechanics and sensor designs or the exploration of the several possible applications in the analysis of respiratory disorders. Therefore, the main objective of this study was to propose a new combination of methods in order to determine the relationship between respiratory signals as a multidimensional data. MethodsIn order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson’s correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. ResultsThe number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. ConclusionIt is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data.

Respiratory rate variability in sleeping adults without obstructive sleep apnea.

Characterizing respiratory rate variability (RRV) in humans during sleep is challenging, since it requires the analysis of respiratory signals over a period of several hours. These signals are easily distorted by movement and volitional inputs. We applied the method of spectral analysis to the nasal pressure transducer signal in 38 adults with no obstructive sleep apnea, defined by an apnea‐hypopnea index <5, who underwent all‐night polysomnography (PSG). Our aim was to detect and quantitate RRV during the various sleep stages, including wakefulness. The nasal pressure transducer signal was acquired at 100 Hz and consecutive frequency spectra were generated for the length of the PSG with the Fast Fourier Transform. For each spectrum, we computed the amplitude ratio of the first harmonic peak to the zero frequency peak (H1/DC), and defined as RRV as (100 − H1/DC) %. RRV was greater during wakefulness compared to any sleep stage, including rapid‐eye‐movement. Furthermore, RRV correlated with the depth of sleep, being lowest during N3. Patients spent most their sleep time supine, but we found no correlation between RRV and body position. There was a correlation between respiratory rate and sleep stage, being greater in wakefulness than in any sleep stage. We conclude that RRV varies according to sleep stage. Moreover, spectral analysis of nasal pressure signal appears to provide a valid measure of RRV during sleep. It remains to be seen if the method can differentiate normal from pathological sleep patterns.

Respiratory Signal Analysis using PCA, FFT and ARTFA (A Generalized Comment)

Respiratory Signal Analysis using PCA, FFT and ARTFA (A Generalized Comment)

Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device

Abnormal breath sounds like wheezes, crackles and stridor at times manifest similar morphologies and pathological features of lung airways obstruction. This may pose problems to proper diagnosis and evaluation of the underlying respiratory condition by human auscultation. In this study, the authors experimented with Time-Frequency threshold-dependent (TFTD) algorithm for detection and classification of breath sounds based on Smartphone. The TFTD algorithm computes important and distinct features of each breath sound using spectro-temporal analysis of recorded lung sounds which can enhance qualitative measurement and quantitative indexing of different respiratory sounds. Several algorithms which run exclusively on desktop computers have been developed for detecting and analyzing specific lung sounds such as wheezes. However, few attempts have been made to perform such analysis on portable devices like mobile phones due to computational complexities and high power consumption associated with the analyses. Our experimental results demonstrate that recent smartphones with improved computational capacity are able to provide comparative performance on analysis of respiratory signals. Furthermore, these phones can serve as convenient tools for measuring and detecting early signs of pulmonary disorders particularly at home and during ambulatory care services where conventional and specialized medical devices may not be accessible.

Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome.

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints.

Ondansetron prevents changes in respiratory pattern provoked by LiCl: A new approach for studying pro-emetic states in rodents?

There are a limited number of biological indices for assessing pro-emetic states in laboratory rodents as they do not possess the vomiting response. In the present study we tested the hypothesis that in rats, pro-emetic intervention would affect the respiratory pattern. To this end, using whole-body plethysmography, in adult male Wistar rats we recorded respiration after i.p. administration of either the emetic agent LiCl or Ringer. Quantification of respiratory signals (from 5 to 35min post-injection) revealed that post-LiCl, mean respiratory rate was significantly lower (126±9 vs. 178±10cpm, p<0.005) and less variable (Kvar 59±8% vs. 73±3%; p<0.05) compared to the post-Ringer condition. Furthermore, while mode values of respiratory rate histograms did not differ between the treatments (indicating that the dominant respiratory frequency remained unchanged), LiCl reduced the fraction of time spent at high respiratory rate (>200cpm) from 25±3% to 9±2% (p=0.004). Thus, reduction of the mean respiratory rate by LiCl was predominantly due to reduced contribution of high-frequency breathing that is normally associated with motor activity and/or arousal. Non-linear multifractal analysis of respiratory signals revealed that post-LiCl, respiration becomes less random and more orderly. 5-HT3 antagonist ondansetron prevented respiratory changes elicited by LiCl. We conclude that the observed changes likely reflect effects of LiCl on animals’ motion, and that this effect is mediated via 5-HT3 receptors. Providing that the effects observed in our study were quite robust, we suggest that simple and non-invasive respiratory monitoring may be a promising approach for studying emesis in rodents.

Relationship between anatomical structure, fractional order models and fractal dimension in the respiratory impedance of healthy patients

This paper presents a theoretical basis for linking the anatomical structure, the values of the fractional order model of the impedance and the values of the fractal dimension extracted from time domain respiratory signal analysis. We employ an electrical ladder network model of the lungs which incorporates their specific morphology and anatomical structure. Measurements of 14 healthy volunteers have been used to provide the respiratory input impedance values using the forced oscillation lung function test. The results indicate that the recurrent ladder network model captures well the impedance in the 7-250Hz frequency interval. Next, we show that the time signals of the respiratory system are coupled to the recurrent ladder network values through the fractal dimension parameter evaluated from pressure-volume loops.

방사선치료 시 자세와 device에 따른 호흡신호의 분석

방사선 치료에서 일정한 호흡주기의 유지가 필요한 흉부 및 복부를 치료할 때 환자의 자세와 belly board device의 사용 여부에 따라 다르게 나타나는 호흡신호의 차이를 통계적으로 분석하여 임상적용에 관한 유용성을 검정하였다. 호흡주기유지는 supine 자세에서 비교적 양호하게 나타났으며, prone 자세에서 호흡주기 유지가 양호하지 않은 경우에 belly board를 적용하여 85%가 유의하게 다른 패턴의 호흡주기를 보였고, 57%는 호흡주기 유지가 양호하게 변화한 것으로 나타났다. 각 피험자의 비만도와 체중에 따른 호흡주기유지 안정성의 차이는 보이지 않았으며, 안정성의 상대적 비교지수로 분석한 시간의 흐름에 따른 호흡의 안정성 유지는 supine 자세에서 비교적 안정적이었으며, 호흡주기 유지의 검정통계결과와 일치하였다(p=0.044, kappa=0.607). 흉부 및 복부 심지어 골반의 방사선치료에서도 호흡에 의한 환자의 움직임은 중요한 고려사항 중 하나이다. 환자의 치료부위와 target의 위치, 예상되는 plan의 beam arrangement에 따라, 환자의 자세 및 device의 여부가 결정되지만, 본 연구와 같이 호흡신호의 통계적 분석과 이의 적용을 통해, 호흡유지에 최적의 자세, belly board와 같은 device 사용여부를 결정하여, 호흡주기의 유지 및 호흡의 안정도 유지에 기여할 수 있을 것으로 사료된다. This study statistically analyzed the difference of the stability of maintaining a respiratory period shown according to position and use of a device to search the tendency and usefulness of a device. The supine position showed better maintaining respiratory cycles than the prone position. The 85% of subjects who showed bad maintenance pattern of a respiratory cycle were significantly different pattern with using belly board. It could be said that there was a significant correlation between the maintenance of a respiratory cycle and relative index of respirational stability(p=0.044, kappa=0.607). The movement due to respiration was one of important considerations in the radiation therapy on chest, abdomen, and even pelvis. This study could contribute to the high quality radiation therapy by statistic analysis of respiratory signals and its application.

Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials

BackgroundSeparation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients.ResultsThirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H2O) and 2. weaning trials with PS: 5 cm H2O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P0.1 and RSBI* P0.1 (conventional model, R2 = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05).ConclusionsWe suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.

SU‐FF‐J‐39: The Impact of Improving Techniques for Phase Reassignment in 4D CT Reconstruction

Purpose: To develop the respiratory signal analysis program for a phase‐based retrospective 4D CT using RPM, spirometer, stereo‐camera and removed the irregular peaks of respiratory signals for accurate phase assignment in 4D CT. Methods: In this study, an in‐house respiratory signal analysis program was developed for the phase reassignment and the analysis of the irregular respiratory signals. Various irregular respiratory patterns were obtained from clinical experimental volunteers. After then, the in‐house program analyzed the factors affecting to phase assignment which is directly related to irradiate sector. The algorithms for separating individual respiratory signals are presented, which made use of baseline signals of input signal to identify the nodes between baseline signals and input signal. Therefore, we could achieve irregular peak reduction and noise elimination in phase assignment. Subsequently, accuracy of phase assignment was improved with removal of irregular signals by self‐developed algorithm. Furthermore, this program also has a function to detect period and amplitude automatically. Result: The standard deviation (SD) of breathing period representing the baseline method was shown stable compare with gradient method, but the difference was not statistically significant. Gradient method shows inaccurate phase assignment when all irregular peaks were detected and assigned. On the other hand, baseline method which was self‐developed algorithm confirmed accurate phase assignment with removal of error peak detection due to tussis. It was also verified superior peak detection with the suggested baseline method at respiratory signals which contained irregular period changes and drifts of amplitude. Conclusion: This study is considered to be useful for not only image reconstruction and elevation of irradiating accuracy through phase assignment of RPM system but also analysis of respiratory signals. Moreover, this respiratory analysis program can be applied to the 4D CT reconstruction using various respiratory sensors for the reduction of motion artifact.

Quality Control of the Ambulatory Polygraphy Using Automatic Analysis

It is necessary to ensure the quality of sleep studies conducted at home given that there can be potential variations. Automatic analysis is simple and could help in an audit. The objective is to find a predictive model of visual reading using an automatic analysis of saturation and respiratory signal in order to establish a reading standard with a polygraph used at home on patients who have sleep apnea-hypopnea clinical symptoms. The analysis was carried out using the following two definitions of hypopnea: an event with a duration of >or= 10 s with a decrease of > 30% of the respiratory signal; and an event associated either with a desaturation of >or= 3% or with a desaturation of >or= 4%. A total of 189 studies were selected from a representative sample of 218 patients. Two pneumologists carried out the readings together. The agreement between the visual respiratory disturbance index (RDI) [ie, apneas plus hypopneas] for both definitions and the automatic respiratory signal analysis (ie, automatic RDI [RDIa]) or the automatic desaturation index of 3% (DI3%a) and of 4% (DI4%a) showed limits from a Bland-Altman plot that were too large. However, a multiple linear regression analysis with RDIa and DI3%a or RDIa and DI4%a presented an acceptable level of agreement with RDI for both definitions (p < 0.001; r(2) = 96.2% and 97%, respectively). The 95% confidence interval for the differences between the RDI and the model was +/- 10.1 or +/- 8.8 events per hour, so a study should be revised outside of these limits. A predictive multiple regression model that uses the automatic analysis of the oximetry and respiratory signal could establish a standard for the visual reading of polygraphy at home.

Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome

The sleep apnea/hypopnea syndrome is a very common sleep disorder, characterised by disrupted breathing during sleep. Depending on the extent of the disruptions to sleep, these are classified as apneas or hypopneas. In order to locate these apneic events an analysis of respiratory signals recorded for an entire night’s sleep is necessary. However, identifying and classifying apneic events is a complex task, given the error associated with the process for digitising signals, variability in expert criteria and the complexity of the signals themselves. This article describes a fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas. The aim is to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty. The ultimate goal was to assist the physician in diagnosing the sleep apnea/hypopnea syndrome. Results in terms of locating events in the polysomnogram showed sensitivity and specificity of 0.87 and 0.89, respectively. A receiver operating curve index of 0.88 was obtained for the classification of events as apneas or hypopneas.

Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat

We applied a novel approach to respiratory waveform analysis--Monotone Signal Segments Analysis (MSSA) on 6-h recordings of respiratory signals in rats. To validate MSSA as a respiratory signal analysis tool we tested it by detecting: breaths and breath-to-breath intervals; respiratory timing and volume modes; and changes in respiratory pattern caused by lesions of monoaminergic systems in rats. MSSA differentiated three respiratory timing (tachypneic, eupneic, bradypneic-apneic), and three volume (artifacts, normovolemic, hypervolemic-sighs) modes. Lesion-induced respiratory pattern modulation was visible as shifts in the distributions of monotone signal segment amplitudes, and of breath-to-breath intervals. Specifically, noradrenergic lesion induced an increase in mean volume (p<or=0.03), with no change of the mean breath-to-breath interval duration (p>or=0.06). MSSA of timing modes detected noradrenergic lesion-induced interdependent changes in the balance of eupneic (decrease; p<or=0.02), and tachypneic (an increase; p<or=0.02) breath intervals with respect to control. In terms of breath durations within each timing mode, there was a tendency toward prolongation of the eupneic (p<or=0.08) and bradypneic-apneic (p<or=0.06) intervals. These results demonstrate that MSSA is sensitive to subtle shifts in respiratory rhythmogenesis not detectable by simple respiratory pattern descriptive statistics. MSSA represents a potentially valuable new tool for investigations of respiratory pattern control.