Ability of single anesthesia for combined robotic-assisted bronchoscopy and surgical lobectomy to reduce time between detection and treatment in stage I non-small cell lung cancer.

Background: Background: Early identification, diagnosis, and treatment of lung cancer is associated with improved clinical outcomes. Robotic-assisted bronchoscopy improves the ability to diagnose early stage lung malignancies and, when combined with robotic-assisted lobectomy under single anesthesia, could reduce time from identification to intervention in early stage lung cancer in a select patient population. Methods: Methods: A retrospective case-control single-center study compared patients with radiographic stage I non-small cell carcinoma (NSCCA) undergoing robotic navigational bronchoscopy and surgical resection (N = 22) with historical controls (N = 63). The primary outcome was time from initial radiographic identification of a pulmonary nodule to therapeutic intervention. Secondary outcomes included times between identification to biopsy, biopsy to surgery, and procedural complications. Results: Results: Patients with suspected stage I NSCCA who received single anesthesia for diagnosis and intervention with robotic-assisted bronchoscopy and robotic-assisted lobectomy had shorter times between identification of a pulmonary nodule and intervention compared to controls (65 vs 116 days, P = 0.005). Cases had lower rates of complications (0% vs 5%) and shorter hospitalizations after surgery (3.6 vs 6.2 days, P = 0.017). Conclusion: Conclusion: Our findings support that implementing a multidisciplinary thoracic oncology team and single-anesthesia biopsy-to-surgery approach in management of stage I NSCCA significantly reduced times from identification to intervention, biopsy to intervention, and length of hospital stays in management of lung cancer.

Systolic pressure response to voluntary apnea predicts sympathetic tone in obstructive sleep apnea as a clinically useful index.

The present investigation tested the hypotheses that systolic arterial pressure (SAP) responses to voluntary apnea (a) serve as a surrogate of sympathetic nerve activity (SNA), (b) can distinguish Obstructive Sleep Apnea (OSA) patients from control subjects and (c) can document autonomic effects of treatment. 9 OSA and 10 control subjects were recruited in a laboratory study; 44 OSA subjects and 78 control subjects were recruited in a clinical study; and 21 untreated OSA subjects and 14 well-treated OSA subjects were recruited into a treatment study. Each subject performed hypoxic and room air voluntary apneas in triplicate. Muscle SNA (MSNA) and continuous AP were measured during each apnea in the laboratory study, while systolic arterial pressure (SAP) responses were measured continuously and by standard auscultation in the clinical and treatment studies. OSA subjects exhibited increased mean arterial pressure (MAP), SAP and MSNA responses to hypoxic apnea (all P<0.01) and the SAP response highly correlated with the MSNA response (R(2)=0.72, P<0.001). Clinical assessment confirmed that OSA subjects exhibited markedly elevated SAP responses (P<0.01), while treated OSA subjects had a decreased SAP response to apnea (P<0.04) compared to poorly treated subjects. These data indicate that (a) OSA subjects exhibit increased pressor and MSNA responses to apnea, and that (b) voluntary apnea may be a clinically useful assessment tool of autonomic dysregulation and treatment efficacy in OSA.

Sleep disordered breathing detection using heart rate variability and R-peak envelope spectrogram.

We report that combining the interbeat heart rate as measured by the RR interval (RR) and R-peak envelope (RPE) derived from R-peak of ECG waveform may significantly improve the detection of sleep disordered breathing (SDB) from single lead ECG recording. The method uses textural features extracted from normalized gray-level cooccurrence matrices of the time frequency plots of HRV or RPE sequences. An optimum subset of textural features is selected for classification of the records. A multi-layer perceptron (MLP) serves as a classifier. To evaluate the performance of the proposed method, single Lead ECG recordings from 7 normal subjects and 7 obstructive sleep apnea patients were used. With 500 randomized Monte-Carlo simulations, the average training sensitivity, specificity and accuracy were 100.0%, 99.9%, and 99.9%, respectively. The mean testing sensitivity, specificity and accuracy were 99.0%, 96.7%, and 97.8%, respectively.

Cross correlation and scatter plots of the heart rate variability and R-peak Envelope as features in the detection of obstructive sleep apnea.

Interbeat heart rate as measured by the RR interval (RR) and R-Peak Envelope (RPE) are two signals that can be extracted from an Electrocardiogram (ECG) with relative ease and high reliability. RR and RPE have been shown to carry markers for detecting sleep disordered breathing (SDB). In this pilot study, we explore the cross correlation of RR and RPE in normal and SDB patients. Nocturnal ECG from 7 normal subjects and 7 SDB patients were used to obtain RR and RPE. The results revealed that the cross correlation of RR and RPE signals is significantly different between normal subjects and SDB patients (p 2x10(-6)). Furthermore, a new scatter plot of RR vs. RPE was developed. Optimum features from the RR vs. RPE scatter plot were extracted and used as input to a multilayer perceptron (MLP) classifier to distinguish between normal and SDB subjects, The detection sensitivity, specificity and accuracy for the training data set were 95.0%, 100.0%, and 97.5%, respectively; and for the test data were 76.6%, 93.2%, and 84.7%, respectively.

Detecting electroencephalography variations due to sleep disordered breathing events.

This study investigates the ability of EEG in identifying apnea/hypopnea events. A preliminary study was performed on 13 subjects (ages: 49.08+/-8.82) previously diagnosed with OSA. Power spectral analysis was performed and centered on apnea/hypopnea event terminations. The normalized power changes between the frequency bands delta, theta, alpha and sigma were calculated using the Welch Averaging Periodogram method between 10 s of EEG data before the event termination and 10 s of EEG data after event termination. A significant decrease in normalized Delta power (1-4 Hz) (p=1.34E-06) and a significant increase in normalized Theta (4-8 Hz) (p=0.0002), Alpha (8-12 Hz) (p=0.0001) and Sigma (12-16 Hz) (p=0.0007) powers were observed across the event terminations. The decrease in Delta is hypothesized as occurring due to an increase in inspiratory effort before events and the presence of an increased alpha/theta/sigma activity after the events indicates the occurrence of an arousal which led to the termination of the event.

ECG biomarkers for simultaneous detection of obstructive sleep apnea and Cheyne-Stokes breathing.

Use of extended electrocardiography (ECG) for detection of sleep disordered breathing SDB when obstructive sleep apneas and Cheyne-Stokes breathing are simultaneously present is explored. A multi-tier algorithm is designed that uses quantitative changes in the morphology of the QRS complex of Lead 1 and V4 due of SDB events and combines those changes with variations in heart rate to detect each type of SDB. For this purpose, ECG signals are divided into 15 minute epochs. These epochs are then subjected to baseline wander removal and R peak detection. An envelope of R peaks is computed to derive R Wave Attenuation (RWA). Concurrently, the heart rate variability (HRV) is also computed. Various biomarkers derived from these trends are combined to develop an algorithm to classify Normal, OSA and CSR epochs. One hundred and five (105) data clips from 15 subjects were used to test the proposed algorithm. It produced detection rates of 93.75%, 100% and 83.3% for Normal, OSA and CSR epochs respectively in case of training set (66 clips). Detection rates of 75%, 85.71% and 70.5% for Normal, OSA and CSR epochs respectively were obtained in case of test set (39 clips).

A method to detect obstructive sleep apnea using neural network classification of time-frequency plots of the heart rate variability.

This paper presents a new method of analyzing time-frequency plots of heart rate variability to detect sleep disordered breathing from nocturnal ECG. Data is collected from 12 normal subjects (7 males, 5 females; age 46+/-9.38 years, AHI 3.75+/-3.11) and 14 apneic subjects (8 males, 6 females; age 50.28+/-9.60 years; AHI 31.21+/-23.89). The proposed algorithm uses textural features extracted from normalized gray-level co-occurrence matrices (NGLCM) of images generated by short-time discrete Fourier transform (STDFT) of the HRV. Using feature selection, seventeen features extracted from 10 different NGLCMs representing four characteristically different gray-level images are used as inputs to a three-layer Multi-Layer Perceptron (MLP) classifier. After a 1000 randomized Monte-Carlo simulations, the mean training classification sensitivity, specificity and accuracy are 99.00%, 93.42%, and 96.42%, respectively. The mean testing classification sensitivity, specificity and accuracy are 94.42%, 85.40%, and 90.16%, respectively.

A new method to detect obstructive sleep apnea using fuzzy classification of time-frequency plots of the heart rate variability.

This paper presents a new method of analyzing time frequency plots of heart rate variability to detect sleep disordered breathing from nocturnal ECG. Data is collected from 12 normal subjects (7 males, 5 females; age 46 +/- 9.38 years, AHI 3.75 +/- 3.11) and 14 apneic subjects (8 males, 6 females; age 50.28 +/- 9.60 years; AHI 31.21 +/- 23.89). The proposed algorithm uses textural features extracted from normalized gray-level co-occurrence matrices (NGLCM) of images generated by short-time discrete Fourier transform (STDFT) of the HRV. Thirty selected features extracted from 10 different NGLCMs representing four characteristically different gray-level images are used as inputs to 10 Fuzzy Logic Systems (FLS) Classifiers. Each FLS is trained and their outputs are combined using a weighed majority rule method. The mean training detection sensitivity, specificity and accuracy are 86.87%, 71.72%, and 79.29%, respectively. The mean testing detection sensitivity, specificity and accuracy are 83.22%, 68.54%, and 75.88%, respectively.

Classification of Cheyne-Stokes breathing and obstructive sleep apnea using ECG.

High cost of diagnostic studies to detect sleep disordered breathing and lack of availability of certified sleep laboratories in all inhabited areas make investigation of alternative methods of detecting sleep disordered breathing attractive. This study aimed to explore the possibility of discerning obstructive sleep apnea (OSA) from Cheyne-Stokes respiration (CSR) using overnight electrocardiography (ECG). Polysomnographic and ECG signals were acquired from the 13 OSA and 7 CSR volunteer subjects. Two signals: R-Wave Attenuation (RWA) and Heart Rate Variability (HRV) series were derived from the ECG. Using frequency domain analysis, various frequency bands in the power spectrum of RWA and HRV signals were identified that showed sensitivity to OSA and CSR events. A three-stage algorithm was developed to detect and differentiate OSA events from CSR events using RWA and HRV analysis. To test the algorithm, the ECG data was divided into fifteen minute epochs for analysis. Seventy two epochs containing OSA and 72 with CSR events were selected. 48 OSA clips and 48 CSR clips were randomly selected to form the training set. The remaining 24 clips in each category formed the test set. This method produced an average sensitivity of 95.83% and specificity of 79.16% in the training set and sensitivity of 87.5% and a specificity of 75% in the test set.

Time domain analysis of R-wave attenuation envelope for sleep apnea detection.

Time domain analysis was carried out on the R-wave attenuation (RWA) envelope of the subjects with and without obstructive sleep apnea. The RWA envelope is derived from the morphology of the electrocardiogram (ECG) obtained during polysomnography data collection of the subjects. Nocturnal polysomnography was performed on 16 normal subjects and 14 obstructive sleep apnea (OSA) patients. The ECG from the polysomnography data was divided into fifteen minute epochs for analysis. The QRS detection was carried out by an algorithm using Hilbert transform. Standard deviation of each of the fifteen one minute epochs in fifteen minute epoch of the RWA envelope was calculated. Standard deviation of these fifteen parameters was observed to have considerably good sensitivity and specificity to sleep apnea. For the clips selected from normal subjects, the parameter produced a sensitivity of 78.57% and specificity of 70.33% for the training set and sensitivity of 87.5% and specificity of 80.95 for the testing set. For the clips selected from OSA subjects, the parameter produced a sensitivity of 72.46% and specificity of 73.53% for the training set and sensitivity of 82.86% and specificity of 66.67% for the testing set.

Periods of intermittent hypoxic apnea can alter chemoreflex control of sympathetic nerve activity in humans.

Obstructive sleep apnea is associated with sustained elevation of muscle sympathetic nerve activity (MSNA) and altered chemoreflex control of MSNA, both of which likely play an important role in the development of hypertension in these patients. Additionally, short-term exposure to intermittent hypoxic apneas can produce a sustained elevation of MSNA. Therefore, we tested the hypothesis that 20 min of intermittent hypoxic apneas can alter chemoreflex control of MSNA. Twenty-one subjects were randomly assigned to one of three groups (hypoxic apnea, hypercapnic hypoxia, and isocapnic hypoxia). Subjects were exposed to 30 s of the perturbation every minute for 20 min. Chemoreflex control of MSNA was assessed during baseline, 1 min posttreatment, and every 15 min throughout 180 min of recovery by the MSNA response to a single hypoxic apnea. Recovery hypoxic apneas were matched to a baseline hypoxic apnea with a similar nadir oxygen saturation. A significant main effect for chemoreflex control of MSNA was observed after 20 min of intermittent hypoxic apneas (P <0.001). The MSNA response to a single hypoxic apnea was attenuated 1 min postexposure compared with baseline (P <0.001), became augmented within 30 min of recovery, and remained augmented through 165 min of recovery (P <0.05). Comparison of treatment groups revealed no differences in the chemoreflex control of MSNA during recovery (P=0.69). These data support the hypothesis that 20 min of intermittent hypoxic apneas can alter chemoreflex control of MSNA. Furthermore, this response appears to be mediated by hypoxia.