Autori prezentujú výsledky klinickej štúdie, v ktorej hodnotili možné použitie ich vlastného anamnestického dolazníka na zisťovanie závažných foriem spánkových porúch dýchania. Mnohonásobnou regresiou selektovali tie parametre dotazníka, ktoré rozhodujúcou mierou prispeli k predikcii prítomnosti závažných foriem spánkových porúch dýchania. Následne vytvorili zjednodušené hodnotenie dotazníka pomocou logickej podmienky. Takéto hodnotenie disponovalo pozitívnou prediktívnou hodnotou 57 % a negatívnou prediktívnou hodnotou až 93 %. Vlastné výsledky porovnali s prácami dosiaľ publikovanými na danú tému, analyzovali možný prínos aj nedostatky nimi zvoleného prístupu. Záverom zhodnotili použitý dotazník, hodnotený prezentovaným spôsobom, ako klinicky použitelný skríningový nástroj pre selekciu pacientov, ktorí s vysokou pravdepodobnosťou netrpia závažným stupňom spánkových porúch dýchania, a nevyžadujú z tejto indikácie ďalšiu polysomnogreifickú diagnostiku.

A clinical study, which evaluates potential use of original anamnestic questionnaire for detection of serious sleep-related breathing disorders, is presented. The for detection of serious sleep-related breathing disorders most important parameters of the questionnaire, were selected using multiple regression. Then a simplified evaluation of the questionnaire results using a logical condition was created. Positive predictive value of such an evaluation was 57 % and its negative predictive value was even 93 %. These results were compared with the papers published yet on the given topic. Both potential contribution and limitations of the chosen method were analyzed. The presented questionnaire and its evaluation is concluded as clinically useful screening tool for the selection of patients, who with high probability do not suffer from serious sleep-related breathing disorders, and who do not need further polysomnographic study for this.

• MeSH
• Adult MeSH
• Research Support as Topic MeSH
• Middle Aged MeSH
• Humans MeSH
• Mass Screening methods   MeSH
• Polysomnography methods   instrumentation   MeSH
• Surveys and Questionnaires methods   MeSH
• Regression Analysis MeSH
• Aged MeSH
• Sensitivity and Specificity MeSH
• Sleep Apnea Syndromes diagnosis   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Review MeSH
• Comparative Study MeSH

Elektrostatická matrace (SCSB) je nová metoda hodnocení poruch ventilace ve spánku založená na principu kondenzátoru, jehož kapacita se mění v závislosti na pohybech pacienta. Autorka předkládá předběžnou evaluaci v diagnostice spánkového apnoického syndromu. Bylo vyšetřeno 6 pacientů s podezřením na poruchy ventilace ve spánku paralelně pomocí polysonmografie i SCSB. Bylo zjištěno, že elektrostatická matrace zaznamenala 82,76 % pauz zjištěných polysonmograficky a naopak 18,73 % pauz polysomnograficky nepotvrzených. Výsledky naznačují použitelnost elektrostatické matrace jako screeningové metody pro detekci spánkového apnoického syndromu.

The use of an electrostatic mattress is a new method in examination of ventilatory disorders during sleep. The principle of this method bases on a condenser, whose capacity changes in relation to the patient's movement. The aim of the study was the evaluation of this mattress in the diagnostics of sleep apnoea syndrome. Author exaiamined 6 patients with a mean body mass index of 32.4 ± 4.6 with a suspicion of sleep apnoea syndrome. Results were compared with a polysomnographic examination in each patient. Using the electrostatic mattress, 82.76 % of apnoea pauses were found compared to the polysomnographic method and 18.73 % of apnoea pauses were detected, not found on polysomnography. In conclusion, results show the usefulness of electrostatic mattress as a screening method for detection of sleep apnoea syndrome.

• MeSH
• Humans MeSH
• Movement MeSH
• Polysomnography methods   MeSH
• Respiration Disorders MeSH
• Posture MeSH
• Sleep MeSH
• Sleep Apnea Syndromes diagnosis   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Comparative Study MeSH

BACKGROUND: An important consequence of sleep-disordered breathing (SDB) is excessive daytime sleepiness (EDS). EDS often predicts a favorable response to treatment of SDB, although in the setting of cardiovascular disease, particularly heart failure, SDB and EDS do not reliably correlate. Atrial fibrillation (AF) is another highly prevalent condition strongly associated with SDB. We sought to assess the relationship between EDS and SDB in patients with AF. METHODS: We conducted a prospective study of 151 patients referred for direct current cardioversion for AF who also underwent sleep evaluation and nocturnal polysomnography. The Epworth Sleepiness Scale (ESS) was administered prior to polysomnography and considered positive if the score was ≥ 11. The apnea-hypopnea index (AHI) was tested for correlation with the ESS, with a cutoff of ≥ 5 events/h for the diagnosis of SDB. RESULTS: Among the study participants, mean age was 69.1 ± 11.7 years, mean BMI was 34.1 ± 8.4 kg/m(2), and 76% were men. The prevalence of SDB in this population was 81.4%, and 35% had EDS. The association between ESS score and AHI was low (R(2) = 0.014, P = .64). The sensitivity and specificity of the ESS for the detection of SDB in patients with AF were 32.2% and 54.5%, respectively. CONCLUSIONS: Despite a high prevalence of SDB in this population with AF, most patients do not report EDS. Furthermore, EDS does not appear to correlate with severity of SDB or to accurately predict the presence of SDB. Further research is needed to determine whether EDS affects the natural history of AF or modifies the response to SDB treatment.

• MeSH
• Atrial Fibrillation complications   MeSH
• Humans MeSH
• Polysomnography MeSH
• Prospective Studies MeSH
• Aged MeSH
• Sensitivity and Specificity MeSH
• Sleep Stages * MeSH
• Sleep Apnea Syndromes complications   diagnosis   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.

• MeSH
• Algorithms MeSH
• Electrocardiography * MeSH
• Humans MeSH
• Respiratory Tract Diseases * MeSH
• Neural Networks, Computer MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: The Berlin Questionnaire (BQ) has been used to identify patients at high risk for sleep-disordered breathing (SDB) in a variety of populations. However, there are no data regarding the validity of the BQ in detecting the presence of SDB in patients after myocardial infarction (MI). The aim of this study was to determine the performance of the BQ in patients after MI. METHODS: We conducted a cross-sectional study of 99 patients who had an MI 1 to 3 months previously. The BQ was administered, scored using the published methods, and followed by completed overnight polysomnography as the "gold standard." SDB was defined as an apnea-hypopnea index of ≥ 5 events/h. The sensitivity, specificity, and positive and negative predictive values of the BQ were calculated. RESULTS: Of the 99 patients, the BQ identified 64 (65%) as being at high-risk for having SDB. Overnight polysomnography showed that 73 (73%) had SDB. The BQ sensitivity and specificity was 0.68 and 0.34, respectively, with a positive predictive value of 0.68 and a negative predictive value of 0.50. Positive and negative likelihood ratios were 1.27 and 0.68, respectively, and the BQ overall diagnostic accuracy was 63%. Using different apnea-hypopnea index cutoff values did not meaningfully alter these results. CONCLUSION: The BQ performed with modest sensitivity, but the specificity was poor, suggesting that the BQ is not ideal in identifying SDB in patients with a recent MI.

• MeSH
• Myocardial Infarction complications   MeSH
• Comorbidity MeSH
• Middle Aged MeSH
• Humans MeSH
• Statistics, Nonparametric MeSH
• Area Under Curve MeSH
• Polysomnography MeSH
• Predictive Value of Tests MeSH
• Cross-Sectional Studies MeSH
• Surveys and Questionnaires * MeSH
• Reproducibility of Results MeSH
• Sensitivity and Specificity MeSH
• Sleep Apnea Syndromes diagnosis   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: Breath detection, i.e. its precise delineation in time is a crucial step in lung function data analysis as obtaining any clinically relevant index is based on the proper localization of breath ends. Current threshold or smoothing algorithms suffer from severe inaccuracy in cases of suboptimal data quality. Especially in infants, the precise analysis is of utmost importance. The key objective of our work is to design an algorithm for accurate breath detection in severely distorted data. METHODS: Flow and gas concentration data from multiple breath washout test were the input information. Based on universal physiological characteristics of the respiratory tract we designed an algorithm for breath detection. Its accuracy was tested on severely distorted data from 19 patients with different types of breathing disorders. Its performance was compared to the performance of currently used algorithms and to the breath counts estimated by human experts. RESULTS: The novel algorithm outperformed the threshold algorithms with respect to their accuracy and had similar performance to human experts. It proved to be a highly robust and efficient approach in severely distorted data. This was demonstrated on patients with different pulmonary disorders. CONCLUSION: Our newly proposed algorithm is highly robust and universal. It works accurately even on severely distorted data, where the other tested algorithms failed. It does not require any pre-set thresholds or other patient-specific inputs. Consequently, it may be used with a broad spectrum of patients. It has the potential to replace current approaches to the breath detection in pulmonary function diagnostics.

• MeSH
• Algorithms * MeSH
• Diagnosis, Computer-Assisted methods   MeSH
• Child MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Signal Processing, Computer-Assisted * MeSH
• Child, Preschool MeSH
• Respiratory Function Tests MeSH
• Check Tag
• Child MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices. Machine learning applied to thermal imaging camera calibration allowed the recognition of its digital information with an accuracy close to 100% for the classification of individual temperature values. The proposed detection of breathing features was used for monitoring of physical activities by the home exercise bike. The results include a decrease of breathing temperature and its frequency after a load, with mean values -0.16 °C/min and -0.72 bpm respectively, for the given set of experiments. The proposed methods verify that thermal and depth cameras can be used as additional tools for multimodal detection of breathing patterns.

• MeSH
• Algorithms MeSH
• Respiration * MeSH
• Image Processing, Computer-Assisted MeSH
• Motion MeSH
• Artificial Intelligence MeSH
• Publication type
• Journal Article MeSH

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.

• MeSH
• Video Recording MeSH
• Time Factors MeSH
• Respiration * MeSH
• Humans MeSH
• Monitoring, Physiologic instrumentation   MeSH
• Movement MeSH
• Heart Rate physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

• MeSH
• Algorithms MeSH
• Biosensing Techniques * MeSH
• Deep Learning * MeSH
• Respiration MeSH
• Electrocardiography MeSH
• Entropy MeSH
• Humans MeSH
• Sleep Apnea, Obstructive MeSH
• Signal Processing, Computer-Assisted * MeSH
• Polysomnography MeSH
• Sleep Wake Disorders * MeSH
• Heart Rate MeSH
• Wavelet Analysis MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

• MeSH
• Respiratory Rate physiology   MeSH
• Adult MeSH
• Respiration MeSH
• Middle Aged MeSH
• Humans MeSH
• Signal Processing, Computer-Assisted MeSH
• Polysomnography methods   MeSH
• Sensitivity and Specificity MeSH
• Sleep physiology   MeSH
• Sleep Apnea Syndromes physiopathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH