Antonio G. Ravelo-García

Application of the Permutation Entropy over the Heart Rate Variability for the Improvement of Electrocardiogram-based Sleep Breathing Pause Detection

In this paper the permutation entropy (PE) obtained from heart rate variability (HRV) is analyzed in a statistical model. In this model we also integrate other feature extraction techniques, the cepstrum coefficients derived from the same HRV and a set of band powers obtained from the electrocardiogram derived respiratory (EDR) signal. The aim of the model is detecting obstructive sleep apnea (OSA) events. For this purpose, we apply two statistical classification methods: Logistic Regression (LR) and Quadratic Discriminant Analysis (QDA). For testing the models we use seventy ECG recordings from the Physionet database which are divided into equal-size learning and testing sets. Both sets consist of 35 recordings, each containing a single ECG signal. In our experiments we have found that the features extracted from the EDR signal present a sensitivity of 65.6% and specificity of 87.7% (auc = 85) in the LR classifier, and sensitivity of 59.4% and specificity of 90.3% (auc = 83.9) in the QDA classifier. The HRV-based cepstrum coefficients present a sensitivity of 63.8% and specificity of 89.2% (auc = 86) in the LR classifier, and sensitivity of 67.2% and specificity of 86.8% (auc = 86.9) in the QDA. Subsequent tests show that the contribution of the permutation entropy increases the performance of the classifiers, implying that the complexity of RR interval time series play an important role in the breathing pauses detection. Particularly, when all features are jointly used, the quantification task reaches a sensitivity of 71.9% and specificity of 92.1% (auc = 90.3) for LR. Similarly, for QDA the sensitivity is 75.1% and the specificity is 90.5% (auc = 91.7).

Human Computer Interactions in Next-Generation of Aircraft Smart Navigation Management Systems: Task Analysis and Architecture under an Agent-Oriented Methodological Approach

The limited efficiency of current air traffic systems will require a next-generation of Smart Air Traffic System (SATS) that relies on current technological advances. This challenge means a transition toward a new navigation and air-traffic procedures paradigm, where pilots and air traffic controllers perform and coordinate their activities according to new roles and technological supports. The design of new Human-Computer Interactions (HCI) for performing these activities is a key element of SATS. However efforts for developing such tools need to be inspired on a parallel characterization of hypothetical air traffic scenarios compatible with current ones. This paper is focused on airborne HCI into SATS where cockpit inputs came from aircraft navigation systems, surrounding traffic situation, controllers’ indications, etc. So the HCI is intended to enhance situation awareness and decision-making through pilot cockpit. This work approach considers SATS as a system distributed on a large-scale with uncertainty in a dynamic environment. Therefore, a multi-agent systems based approach is well suited for modeling such an environment. We demonstrate that current methodologies for designing multi-agent systems are a useful tool to characterize HCI. We specifically illustrate how the selected methodological approach provides enough guidelines to obtain a cockpit HCI design that complies with future SATS specifications.

Cepstrum Coefficients of the RR Series for the Detection of Obstructive Sleep Apnea Based on Different Classifiers

Two automatic statistical methods for the classification of the obstructive sleep apnoea syndrome based on the cepstrum coefficients of the RR series obtained from the Electrocardiogram (ECG) are presented. We study the effect of working with Linear Discriminant Analysis (LDA) and compare its performance with a reference detector based on Support Vector Machines (SVM). These classifications methods require two previous stages: preprocessing and feature extraction. Firstly, R instants are detected previous to the feature extraction phase thanks to a preprocessing over the ECG. Secondly, Cepstrum Coefficients over the RR signal is applied to extract the relevant characteristics specially those related to the system modelled by the filter-type elements concentrated in the low time lag region.

A portable wireless device based on oximetry for sleep apnea detection

Obstructive sleep apnea is a highly prevalent sleep related breathing disorder and polysomnography is the gold standard exam for diagnosis. Despite providing results with high accuracy this multi-parametric test is expensive, time consuming and does not fit with the new tendency in health care that is changing the focus to prevention and wellness. Home health care is seen as a possible way to address this problematic by using minimal invasive devices, providing low cost of diagnosis and higher accessibility. To address this, a portable and automated sleep apnea detector was designed and evaluated. The device uses one SpO2 sensor and the analysis is based on the connection between oxygen saturation and apnea events. The measured signals are received in a field-programmable gate array that checks for errors and implements the communication protocols of two wireless transmitters. Two solutions were implemented for processing the data: one based on a smartphone (due to availability and low cost) and another based on a personal computer (for a higher computation capability). The algorithms were implemented in Java, for the smartphone, and in Python, for the computer. Both implementations have a graphical user interface to simplify the device operation. The algorithms were tested using a database consisting of 70 patients with the SpO2 signal collected in a Hospital. The algorithm performance achieved an average accuracy, sensitivity and specificity of 87.5, 79.5 and 90.8% respectively.

Automatic Detection of a Phases for CAP Classification.

The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then applying a finite state machine to implement the final classification. A public database was used to test the algorithms and a total of eleven features were analysed. Sequential feature selection was employed to select the most relevant features and a post processing procedure was used for further improvement of the classification. The classification of the A phases was produced using linear discriminant analysis and the average accuracy, sensitivity and specificity was, respectively, 75%, 78% and 74%. The cyclic alternating pattern detection accuracy was 75%. When comparing with the state of the art, the proposed method achieved the highest sensitivity but a lower accuracy since the fallowed approach was to keep the REM periods, contrary to the method that is used in the majority of the state of the art publications which leads to an increase in the overall performance. However, the approach of this work is more suitable for automatic system implementation since no alteration of the EEG data is needed.

Transfer entropy to characterize brain-heart topology in sleep apnea patients treated with continuous positive airway pressure

Transfer entropy (TE) is a nonlinear metric employed recently in polysomnography (PSG) recordings to quantify the topological characteristics of the brain-heart physiological network. The present study applies the TE to evaluate its usefulness to identify quantitative differences in PSG registers of patients diagnosed with oclusive sleep apnea (OSA), before and after a continuous positive air pressure (CPAP) therapy. PSG recordings corresponding to 19 OSA patients were analysed under the rationale that the set of EEG subbands represents the sympathetic activity of the autonomic nervous system (ANS), and the high frequency component of the heart rate variability (HRV) represents the parasympathetic activity. The TE was computed based on a binning estimation and the results were analyzed via effect size calculation. The results showed that the sympathetic activity is increased in the presence of OSA, which is represented by the increased flow of information among brain subsystems and dropping to values close to zero during CPAP therapy. In contrast, the parasympathetic activity showed to be reduced in the presence of OSA and augmented during the CPAP therapy.

Symmetry Extraction in High Sensitivity Melanoma Diagnosis

Melanoma diagnosis depends on the experience of doctors. Symmetry is one of the most important factors to measure, since asymmetry shows an uncontrolled growth of cells, leading to melanoma cancer. A system for melanoma detection in diagnosing melanocytic diseases with high sensitivity is proposed here. Two different sets of features are extracted based on the importance of the ABCD rule and symmetry evaluation to develop a new architecture. Support Vector Machines are used to classify the extracted sets by using both an alternative labeling method and a structure divided into two different classifiers which prioritize sensitivity. Although feature extraction is based on former works, the novelty lies in the importance given to symmetry and the proposed architecture, which combines two different feature sets to obtain a high sensitivity, prioritizing the medical aspect of diagnosis. In particular, a database provided by Hospital Universitario de Gran Canaria Doctor Negrin was tested, obtaining a sensitivity of 100% and a specificity of 66.66% using a leave-one-out validation method. These results show that 66.66% of biopsies would be avoided if this system is applied to lesions which are difficult to classify by doctors.

Pulse rate trends in obstructive sleep apnea: a reliable tool to predict long term response to CPAP?

To the editor, A predictive model of long term response to CPAP could be a useful tool for the clinician. In this regard, previous studies considered that changes in heart rate variability (HRV) are a key variable to assess continuous positive airway pressure (CPAP) response (1,2). We read with interest the recent article by Pengo MF et al. (3) were authors hypothesized that baseline nocturnal pulse rate (PR) trends help predict long term response to CPAP. In this study, main findings were improvement in daytime sleepiness among obstructive sleep apnea (OSA) patients with baseline negative change in PR contrary to OSA patients with positive change in PR. Nonetheless, we consider that a few aspects of this study deserve reviewing. First, the diagnostic modality was unattended pulse oximetry. So far, the published data on this method is difficult to compare because of differences in the parameters measured and differences in the reference standard. Some studies reported high specificity while others reported a high sensitivity. For screening purposes, both high sensitivity and high pretest likelihood of OSA are needed. Additionally, age, pulmonary function, and degree of obesity impact on nocturnal desaturation and also influence sensitivity and specificity. Therefore, for research purposes, diagnosis should be confirmed by polygraphy or polysomnography (4-6). In Pengo MF et al. patients in the OSA group had lower mean saturation of hemoglobin (SpO2) than controls (92.7% vs. 95.4%). This difference makes the OSA group more likely to desaturate during sleep just by virtue of the oxyhemoglobin dissociation curve dynamics (5). Furthermore, some of the OSA patients may have suffered from other comorbidities such as obesity hypoventilation syndrome. This was more likely in the OSA group with positive change PR, where patients were more obese (122+/–26 kg) and had lower mean SpO2 (90.9%) compared to those with negative change PR (107+/–26 kg, and 93.2%, respectively).

Devices for home detection of obstructive sleep apnea: A review

One of the most common sleep-related disorders is obstructive sleep apnea, characterized by a reduction of airflow while breathing during sleep and cause significant health problems. This disorder is mainly diagnosed in sleep labs with polysomnography, involving high costs and stress for the patient. To address this situation multiple systems have been proposed to conduct the examination and analysis in the patients home, using sensors to detect physiological signals that are examined by algorithms. The objective of this research is to review publications that show the performance of different devices for ambulatory diagnosis of sleep apnea. Commercial systems that were examined by an independent research group and validated research projects were selected. In total 117 articles were analysed, including a total of 50 commercial devices. Each article was evaluated according to diagnostic elements, level of automatisation implemented and the deducted level of evidence and quality rating. Each device was categorized using the SCOPER categorization system, including an additional proposed category, and a final comparison was performed to determine the sensors that provided the best results.

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold.

Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the “Apnea-ECG Physionet database” and the “HuGCDN2014 database” are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.