Search

Cervical cancer constitutes an issue in public health, becoming the leading cause of death by cancer in women between 20-40 years of age in Latin America. In Argentina 5000 new cases are diagnosed each year, where more than 56% are in advanced stages. The aim of the present current opinion or critical review article is to remark the importance of the prognostic significance of the Central Tumor Size in stages IIB and IIIB cervical cancer, as well as to propose a new FIGO Staging System for Cervical cancer and trying to find out a role for the different therapeutic strategies for those cases.

An assisted method to segment Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT) from Magnetic Resonance Imaging (MRI) slices is presented. The segmentation process, called shape-based segmentation, consists in three main steps: 1) to draw a series of closed curves at different slices that separates the abdominal structures of interest, 2) to generate a 3D model from the closed curves for each abdominal structure by using shape-based interpolation and 3) to apply a segmentation algorithm to define the adipose tissue. The 3D models considerably simplify the problem since the abdominal structures are separated, and in turn, this reduces the possibility of large segmentation errors. In addition, a fully automatic segmentation procedure was also implemented. Twenty slices of MRI at the abdominal region for each of twelve subjects were analysed. The results of the shape-based and automatic segmentation were compared with the expert segmentation carried out in the slice located at the umbilicus level. Correlation Coefficient (CC) and volume error (VE) were used as performance measures. The comparison between the expert and shape-based segmentation for SAT yielded results of CC= 0.974 and VE=-0.01 ± 5.8 cm3, while for VAT the performance indexes were CC= 0.993 and VE= 0.9 ± 1.8 cm3. The results suggest that the shape-based segmentation provides an accurate and simple assessment of the abdominal adiposity with minimal human intervention and it could be used as a simple tool in clinics.

Parkinson’s disease is a neurodegenerative pathology difficult to diagnose. Researches have confirmed the presences of death cells in the brain produced by the modification of a protein called alpha-synuclein synuclein in people with Parkinson disease. Currently, a great amount of research is conducted to identify its biomarkers for early diagnostics. Recently, a studio found differences between the alpha- synuclein of the skin from Parkinson’s disease and normal patients. In this paper, we use Raman spectroscopy through a numerical model to simulate the vibrational modes of well-defined finite clusters of alpha-synuclein in normal and pathological state, using the Gaussian09 software. The results of the model in the range of x − y cm−1 are in good agreement with the experimental Raman spectra acquired from human skin with alpha-synuclein in the normal and pathological state.

Computerized Perimetry (CP) is one of the clinical tests commonly used to evaluate peripheral vision and monitor the progress of eye diseases such as glaucoma. The aim of CP is to determine retinal sensitivity using luminous stimuli of variable intensity at different positions of the visual field. In modern campimetry devices, patients must respond to each perceived stimulus by pressing a button; however, this characteristic makes the test more susceptible to spurious and erroneous interpretations due to tiredness, lack of concentration, or device design flaws. This work presents an alternative paradigm for automatically assessing stimulus perception through a low-cost eye tracker and a computer monitor. We tested the preliminary version of the paradigm among eight subjects and obtained favorable results. In conclusion, our eye-tracking paradigm tool could help design more reliable visual field tests using low-cost portable equipment.

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA ([Formula: see text]) outperforms VRA ([Formula: see text]) for moderate to high noise levels whereas mVRA outperformed EMD ([Formula: see text]) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results ([Formula: see text]. For Raman spectra of synthetic material, good performance of the three methods ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and progress monitoring of MS is performed by the aid of T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying results. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues and may also reduce the execution time, while the GMMF classifier is robust to noise and also computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results without requiring user assistance nor anatomical prior information.

Background: Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disorder characterized by deterioration of the substantia nigra, resulting in a deficiency of dopamine. PD is considered a movement disorder associated with numerous non-motor symptoms related to Autonomic Nervous System failures which can precede the motor ones. Therefore, their awareness could be helpful in the diagnosis of PD at an early stage. Methods: Heart Rate Variability (HRV) is assessed by time and frequency domain indices, and by nonlinear indices based on symbolic dynamics and multiscale symbolic entropy. The features obtained were used to classify between PD patients and control volunteers using a support vector machine. Volunteers performed cardiovascular autonomic reflex tests: active standing, post- hyperventilation and controlled breathing. Results: Temporal and frequency indices showed significantly lower values in PD patients compared to control volunteers. Symbolic dynamics and multiscale symbolic entropy results suggest a decrease in the complexity of the HRV signal in PD patients, in contrast with a more variable pattern of words for control volunteers. During controlled breathing differences between groups were found with most of the indices computed. Additionally, classification process achieves good separability during cardiorespiratory maneuvers (>95% of accuracy) and features based on symbolic dynamics showed high discrimination between groups. Conclusions: The results found in this work suggest that the proposed methodological approach can classify PD patients in an early disease stage from healthy controls and give additional information about the cardiorespiratory system, which could be useful for diagnosis and follow up of PD patients.

In the present study, a comparison of the heart rate variability (HRV) behavior between healthy subjects and Nocturnal Front Lobe Epilepsy (NFLE) patients was carried out during the A-phases of sleep. The A-phases are short cortical events that interrupt the basal oscillation of the sleep stages and form the cyclic alternating pattern phenomenon. HRV was assessed by means of standard temporal measures and frequency measures based on time-varying autoregressive (TVAR) models. The analysis of HRV, in relation to the A-phases occurrence, was performed selecting two segments: one before the onset of the A-phase and one during the A-phase time. The results showed a significant increment in the heart rate during the A-phases in both, healthy subjects and NFLE patients. In addition, a major participation of the sympathetic nervous system was found in both healthy and pathologic conditions based on the sympatho-vagal index (LF/HF) during A-phases. The intensity of the shift towards sympathetic activity is related of A-phase type, where the largest shift is found in A3 phases. However, the NFLE patients present a weaker autonomic response during A-phases. The results suggest that the autonomic cardiac response related with the surveillance mechanism of NFLE patients is affected.

This paper presents a study of the heart rate variability (HRV) and respiratory rate variability (RRV) during daytime and nighttime sleep, considering the sleep stages. Eleven healthy female shift workers of 20–54 years old, were recorded during the sleep period. Detrended fluctuation analysis (DFA) was used to assess the short and long-range correlations of time series. Furthermore, the bivariate signal (HRV - RRV) was evaluated by means of the multivariate DFA. The results showed significant differences between the sleep stages. Nevertheless, the mean values of the HRV and RRV exponents did not show meaningful variability between daytime and nighttime sleep. The results suggest that there are changes in the central nervous system, in the regulation of heart and respiratory rate during the sleep stages. Also, the correlations of nighttime sleep are preserved during daytime sleep, which could be due to the adaptability of sleep to changes due to shifting work, keeping correlation properties of HRV and RRV.

Sleep is an essential process in our life, which covers 1/3 of our lifetime. But this process can be affected by disorders producing serious consequences at physiological and behavioral level. One of the major indexes connected to the sleep disorders is the dynamic of the sleep macrostructure that is used for the assessment of sleep quality. Beyond sleep macrostructure, recently attention is also given to a finer structure of sleep called Cyclic Alternating Pattern (CAP). CAP is composed by short cortical events (A-phases), where some transition processes can be observed. With the aim to unveil properties of this transition phenomenon, in this work, we present a wavelet singularity analysis of the EEG signal during the onset and offset of A-phases. The results showed that EEG signal presents significant differences between A-phases and activity of background when the average singularity is considered. This finding can help both in better delineating the A-phases of CAP sleep and in understanding the mechanisms behind the CAP dynamics.

Objectives The aims of the present study were: 1) to define the prognostic significance of the CTS in stages IIB and IIIB cervical cancer and its impact on disease-free survival (DFS) and overall survival (OS) rates; 2) to propose a new FIGO Staging System for CC based on this evidence. Methods Retrospective study including 450 pts. between 1/2007 to 9/2011 FIGO stages IIB (229 pts.) and IIIB (221 pts.) cervical cancer (cc) . MRI was added to mesure the CTS. It was stratified in ≤-3.99 cm, 4–5.99 cm, 6–7.99 cm & >8 cm. The disease-free survival (DFS) and overall survival (OS) related to tumor size.were analyzed . Results The median age were 45 & 49 years old for stages IIB & IIIB groups. Median Follow up: 45 months. The DFS & OS rates were: stage IIb,58.5% & 64%; and 40% and 43% for the IIIb,respectively The DSF and OS rates were 65,1% and 71,5% in CTS between 4- 5.99cm, vs 27,7% and 36.6% for CTS >6 cm (p 6 cm (p Conclusions Patients with CTS >6 cm ihad a worse prognosis. These FIGO Stages could be modified into IIb1 & IIb2; and IIIb1 & IIIb2, with a 6m cut off. It is needed a personalized treatment strategy for those cases.

Objectives To assess the impact of age as a determining prognostic factor in the survival of women with uterine cervical cancer (UCC). Methods A retrospective observational study was carried out in which patients who consulted in Oncological Hospital of Buenos Aires, in the period between January 2007 and December 2011. The variables were studied: age and tumor stage. Three age groups were established: patients 60. For the analysis of disease-free survival (DFS) and overall survival (OS), the Kaplan-Meier method was used and for the comparison between the different groups, the Log Rank test method using the statistical package IBM Statics Version 21. A statistically significant P value of ≤0.05 was considered. Results 748 patients were included, of which 145 (19.4%) 60 years. The median general age was 46 years. The general OS was 59.3% (P=0.04), 48.3%, 63.3% and 57.4% respectively in the groups. The general DFS was 55.5% (P=0.04), in the groups: 45.8%, 59% and 53.6% respectively. The distribution by stages was predominantly locally advanced: IIB 30.6% (229), IIIB 29.5% (221), being statistically significant for OS stages IB2, IIA2, IIB, IIIA and IVA. Conclusions In addition to the widely known prognostic factors, age also seems to be an important factor, although probably underestimated by current treatment guidelines. There is a population of patients with UCC whose diagnosis at an early age implies an alarming prognosis.

Objectives To evaluate the overvall survival (OS)and disease free survival (DFS) rates related to surgical radicality in the ART sparing the uterine arteries and hypogastric plexus in patiens with early stage cervical cancer(CC). Methods Twenty seven pts. FIGO stages Ia2 & Ib1, were included between 10/04 a 10/15. Stages Ib1 >2 cm and An ART sparing the uterine arteries and hypogastric plexus was performed. Surgical radicality was compared with an historical control group of C1 radical hysterectomies. The OS& DFS rates, complications, pregnancies, recurrences, and follow-up,were analyzed. Results The ART was performed in 25/27 patients (95,2%). Age: 27,5 years (25–35). Five patients received NCH. In 2 cases the procedure had to be completed with a C1RH because of positive margins in the surgical specimen. Radicality (surgical specimen): tumor size 2,36 (1,-4,1) cm; Vaginal length 2,62 (0,8–3,1) cm; Right parametrium 3,2 (1,8–3,9) cm; left parametrium 3,04 ( 2,3–3,5) cm; cervix length 2,8 (2,3–3,3) cm; Lymph nodes 13,5 (2–22). No statisticaly differences were observed in surgical radicality between ART vs C1RH. Complications: 1 case of dyspareunia + cervical polyposis. 1 case of dysmenorrhea. Four pregnancies were observed. Three healthy newborns and 1 abortion were registered,all in NCH cases). Recurrences: 2/27 (8,4%). Deaths: 2/27 (7,3%) . DFS & OS: 92,5%. Follow-up: 127 months (85–180). Conclusions The ART sparing the uterine arteries and hypogastric plexus (C1 ART), showed to be feasible and oncologicaly safe. This variant of ART, could be considered as an alternative to C1RH for these initial FIGO stages.

People with obstructive sleep apnea hypopnea syndrome (OSAHS) are affected by disruption in normal breathing patterns during sleep. In the literature, it is common to find acquisition of Thoracic (THO) and abdominal (ABD) movements with piezo-electric bands included in a full polysomnography. These movements convey valuable information related to sleep apnea events, and for this reason, contactless methods, such as the Pressure Bed Sensor (PBS), have been developed to extract this information. The main goal of this study is to analyze apnea and hypopnea fluctuations based on the spectral analysis of nasal airflow measure (as a reference signal), thoraco–abdominal effort and PBS respiration signal. To this end, features from the respiratory spectrum such as entropy, Gaussian modeling and instantaneous frequency were computed. These spectral properties were evaluated in three windows for each sensor: control point (CP) which is a window randomly extracted for the sleep time without apnea event, before event (BE) a window before an apnea episode and during event (DE) a window during an apnea episode. Apnea and hypopnea events were analyzed separately. According to a database of seventeen subjects, DE windows showed significant differences with respect to the CP window in most of the computed indices for both apnea and hypopnea events for all sensors. Significant differences were also found when DE and BE windows were compared in the case of apnea for all the sensors. In conclusion, the analyzed spectral characteristics could be a good tool to detect apnea and hypopnea. Finally, PBS signal which is a unobtrusive sensor, maintains the spectral properties of the standard respiratory effort measurements, and the use of this sensor could be useful for the monitoring outside of a clinical environment, simplifying the acquisition process.

A series of short events, called A-phases, can be observed in the human electroencephalogram during NREM sleep. These events can be classified in three groups (A1, A2 and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers., Comment: 19 pages, 5 figures, 4 tables

Objective A-phases are short-time cortical events during sleep that repeatedly disrupt the basal fluctuations of electrical brain activity. They are the basic units of the cyclic alternating pattern (CAP), which is related to the instability and consolidation of the sleep process. The main purpose of this study is to evaluate the temporal occurrence of A-phases (TOAP) under normal and pathologic sleep conditions. Methods To derive a quantitative description of TOAP, we have applied detrended fluctuation analysis (DFA), to unveil scale-free behavior from non-stationary time series. Data from sleep recordings of 15 healthy subjects (H), 37 with nocturnal frontal lobe epilepsy (NFLE), 9 with periodic leg movement (PLM), and 22 with REM Sleep Behavior Disorder (RBD) from the Physionet database were used. TOAP was computed from binary time series constructed from A-phase annotations, where symbols 1 and −1, represent the presence or absence of an A-phase, respectively. These time series were analyzed through DFA and characterized by the scaling exponent. Results In all cases, numerical results show evidence that a statistical fractal structure is embedded in TOAP, where persistent scaling behavior is observed with scaling exponent close to 1. Conclusion The sleep process maintains a similar structure from the A-phase perspective despite of internal or external factors that could affect the sleep process, suggesting that sleep may be a resilient process. Significance The scaling exponent of the A-phase occurrence provides new information about the sleep process that may have clinical relevance and complementary to standard clinical indices such as the CAP-rate.

In this work, the scaling behavior of the sleep process is evaluated by using detrended fluctuation analysis based on wavelets. The analysis is carried out from arrivals of short and recurrent cortical events called A-phases, which in turn build up the Cyclic Alternating Pattern phenomenon, and are classified in three types: A1, A2 and A3. In this study, 61 sleep recordings corresponding to healthy, nocturnal frontal lobe epilepsy patients and sleep-state misperception subjects, were analyzed. From the A-phase annotations, the onsets were extracted and a binary sequence with one second resolution was generated. An item in the sequence has a value of one if an A-phase onset occurs in the corresponding window, and a value of zero otherwise. In addition, we consider other different temporal resolutions from 2[Formula: see text]s to 256[Formula: see text]s. Furthermore, the same analysis was carried out for sequences obtained from the different types of A-phases and their combinations. The results of the numerical analysis showed a relationship between the time resolutions and the scaling exponents; specifically, for higher time resolutions a white noise behavior is observed, whereas for lower time resolutions a behavior towards to [Formula: see text]-noise is exhibited. Statistical differences among groups were observed by applying various wavelet functions from the Daubechies family and choosing the appropriate sequence of A-phase onsets. This scaling analysis allows the characterization of the free-scale dynamic of the sleep process that is specific for each sleep condition. The scaling exponent could be useful as a diagnosis parameter in clinics when sleep macrostructure does not offer enough information.

In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed. Graphical Abstract ?.

An analysis of the EEG signal during the B-phase and A-phases transitions of the cyclic alternating pattern (CAP) during sleep is presented. CAP is a sleep phenomenon composed by consecutive sequences of A-phases (each A-phase could belong to a possible group A1, A2 or A3) observed during the non-REM sleep. Each A-phase is separated by a B-phase which has the basal frequency of the EEG during a specific sleep stage. The patterns formed by these sequences reflect the sleep instability and consequently help to understand the sleep process. Ten recordings from healthy good sleepers were included in this study. The current study investigates complexity, statistical and frequency signal properties of electroencephalography (EEG) recordings at the transitions: B-phase--A-phase. In addition, classification between the onset-offset of the A-phases and B-phase was carried out with a kNN classifier. The results showed that EEG signal presents significant differences (p < 0.05) between A-phases and B-phase for the standard deviation, energy, sample entropy, Tsallis entropy and frequency band indices. The A-phase onset showed values of energy three times higher than B-phase at all the sleep stages. The statistical analysis of variance shows that more than 80% of the A-phase onset and offset is significantly different from the B-phase. The classification performance between onset or offset of A-phases and background showed classification values over 80% for specificity and accuracy and 70% for sensitivity. Only during the A3-phase, the classification was lower. The results suggest that neural assembles that generate the basal EEG oscillations during sleep present an over-imposed coordination for a few seconds due to the A-phases. The main characteristics for automatic separation between the onset-offset A-phase and the B-phase are the energy at the different frequency bands.

We evaluate the performance of an unobtrusive sleep monitoring system in the detection of the sleep apnea-hypopnea syndrome (SAHS). The proposed system is a pressure bed sensor (PBS) that incorporates multiple pressure sensors into a bed mattress to measure several physiological signals of the sleeping subject: respiration; heart rate; and body movements. An automatic algorithm is developed to calculate a respiratory event index (REI). The recordings of 24 patients with suspected sleep problems are analyzed, and the results are compared with the gold standard methods; first with manual scoring of polysomnography to calculate the apnea-hypopnea index (AHI), and second with automatic detection of REI from the respiratory inductive plethysmography belts. The correlation coefficient between AHI and REI from PBS is up to 0.93. Evaluating the ability of PBS in the diagnosis of pathologic (AHI $\ge 5$ ) and nonpathologic ( ${\rm AHI} ) subjects, we obtained a sensitivity, specificity, and accuracy of 100%, 92%, and 96%, respectively. To diagnose three levels of SAHS, mild, moderate, and severe, the Cohen’s kappa value is 0.76. These findings support that PBS recording could provide a simple and unobtrusive method for detection of SAHS in home monitoring.

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

Objective The aim of this study is to provide an improved method for the automatic classification of the Cyclic Alternating Pattern (CAP) sleep by applying a segmentation technique to the computation of descriptors from the EEG. Methods A dataset of 16 polysomnographic recordings from healthy subjects was employed, and the EEG traces underwent first an automatic isolation of NREM sleep portions by means of an Artificial Neural Network and then a segmentation process based on the Spectral Error Measure. The information content of the descriptors was evaluated by means of ROC curves and compared with that of descriptors obtained without the use of segmentation. Finally, the descriptors were used to train a discriminant function for the automatic classification of CAP phases A. Results A significant improvement with respect to previous scoring methods in terms of both information content carried by the descriptors and accuracy of the classification was obtained. Conclusions EEG segmentation proves to be a useful step in the computation of descriptors for CAP scoring. Significance This study provides a complete method for CAP analysis, which is entirely automatic and allows the recognition of A phases with a high accuracy thanks to EEG segmentation.

Characterization of living tissue without the need for biopsies is the goal of several probe technologies such as Multi- spectral Fluorescence Lifetime Imaging Microscopy. This technique measures the mixed response from the endogenous fluorophores within an organic sample. This response is decomposed into the individual response from every constituent using a fully constrained linear unmixing algorithm: Blind End-member and Abundance Extraction (BEAE). Further validation of the method is needed specially when dealing with real laboratory samples. Moreover, the BEAE method incorporates a regularization parameter during the quadratic optimization procedure which has to be tuned to improve the estimation accuracy. Different values for the regularization parameter are tested using synthetic data at a signal-to-noise ratio of 10 dB and 15 dB. The relative error against the ideal end-members for each component is measured. Results show that the estimation accuracy in each end-member increases when the regularization parameter is around 0.75. Blind decomposition of m-FLIM data from coronary samples is also performed for validation purposes. The extracted fluorescence decays are identified as collagen, elastin and LDL responses. Histopatology slides are used as reference to validate the results. Synthetic simulation shows that the BEAE algorithm performs a more accurate estimation of the end-members profiles due to the regularization term. Furthermore, analysis performed on ex-vivo samples match the qualitative description provided by histopatology slides.

Approximate string matching (ASM) is a well-known computational problem with important applications in database searching, plagiarism detection, spelling correction, and bioinformatics. The two main issues with most ASM algorithms are (1) computational complexity, and (2) low specificity due to a large amount of false positives being reported. In this paper, a very efficient ASM method is proposed, along with a post -processing stage designed to significantly reduce the amount of false positives. Results with random strings show that the proposed method is capable of performing a search within a large (1 M b) string in about 100 ms, with a sensitivity and specificity of nearly 100%.

Raman spectroscopy of biological tissue presents fluorescence background, an undesirable effect that generates false Raman intensities. This paper proposes the application of the Empirical Mode Decomposition (EMD) method to baseline correction. EMD is a suitable approach since it is an adaptive signal processing method for nonlinear and non-stationary signal analysis that does not require parameters selection such as polynomial methods. EMD performance was assessed through synthetic Raman spectra with different signal to noise ratio (SNR). The correlation coefficient between synthetic Raman spectra and the recovered one after EMD denoising was higher than 0.92. Additionally, twenty Raman spectra from skin were used to evaluate EMD performance and the results were compared with Vancouver Raman algorithm (VRA). The comparison resulted in a mean square error (MSE) of 0.001554. High correlation coefficient using synthetic spectra and low MSE in the comparison between EMD and VRA suggest that EMD could be an effective method to remove fluorescence background in biological Raman spectra.

This work aims to investigate new markers for the quantitative characterization of insomnia, in the context of sleep microstructure, as expressed by cyclic alternating pattern (CAP) sleep. The study group includes 11 subjects with normal sleep and 10 subjects with diagnosed primary insomnia. Differences between normal sleepers and insomniacs are investigated, in terms of dynamics and content of CAP events. The overall rate of CAP and of different phases is considered. The dynamic in the structure and alternation of CAP events is further studied in different scales by use of wavelet analysis, and calculation of energy/entropy features. The content of CAP events is studied in terms of electroencephalography (EEG) complexity analysis for the different types of events. Statistically significant differences are highlighted, both in structure and content. Besides confirming the increase in CAP rate, main findings regarding the microstructure difference in insomnia include: 1) as regards the deep sleep building phases, more irregular activation-deactivation patterns, with bigger deactivation time, i.e., distance between consecutive activation events, and appearing with higher EEG complexity in deactivation, and 2) a bigger duration of desynchronisation phases, with increased EEG complexity and more irregular patterns. This analysis extends previous findings on the relation between CAPrate increase and sleep instability mechanisms, proposing specific features of CAP that seem to play a role in insomnia (as consistently presented via classification analysis). This opens new perspectives for the understanding of the role of CAP in the quantitative characterization of sleep and its disorders.

The aim of this study was to propose a novel automatic method for quantifying motor-tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three-axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video-based ones provided by expert clinicians, which were taken as the gold-standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland-Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video-based systems. Furthermore, there was substantial concurrency with the gold-standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor-tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long-term monitoring out of clinical environments.

We describe a system for the evaluation of the sleep macrostructure on the basis of Emfit sensor foils placed into bed mattress and of advanced signal processing. The signals on which the analysis is based are heart-beat interval (HBI) and movement activity obtained from the bed sensor, the relevant features and parameters obtained through a time-variant autoregressive model (TVAM) used as feature extractor, and the classification obtained through a hidden Markov model (HMM). Parameters coming from the joint probability of the HBI features were used as input to a HMM, while movement features are used for wake period detection. A total of 18 recordings from healthy subjects, including also reference polysomnography, were used for the validation of the system. When compared to wake-nonrapid-eye-movement (NREM)-REM classification provided by experts, the described system achieved a total accuracy of 79+/-9% and a kappa index of 0.43+/-0.17 with only two HBI features and one movement parameter, and a total accuracy of 79+/-10% and a kappa index of 0.44+/-0.19 with three HBI features and one movement parameter. These results suggest that the combination of HBI and movement features could be a suitable alternative for sleep staging with the advantage of low cost and simplicity.

This paper presents a method for obstructive sleep apnea (OSA) screening based on the electrocardiogram (ECG) recording during sleep. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways and this phenomenon can usually be observed also in other peripheral systems such as the cardiovascular system. Then the extraction of ECG characteristics, such as the RR intervals and the area of the QRS complex, is useful to evaluate the sleep apnea in noninvasive way. In the presented analysis, 50 recordings coming from the apnea Physionet database were used; data were split into two sets, the training and the testing set, each of which was composed of 25 recordings. A bivariate time-varying autoregressive model (TVAM) was used to evaluate beat-by-beat power spectral densities for both the RR intervals and the QRS complex areas. Temporal and spectral features were changed on a minute-by-minute basis since apnea annotations where given with this resolution. The training set consisted of 4950 apneic and 7127 nonapneic minutes while the testing set had 4428 apneic and 7927 nonapneic minutes. The K-nearest neighbor (KNN) and neural networks (NN) supervised learning classifiers were employed to classify apnea and non apnea minutes. A sequential forward selection was used to select the best feature subset in a wrapper setting. With ten features the KNN algorithm reached an accuracy of 88%, sensitivity equal to 85%, and specificity up to 90%, while NN reached accuracy equal to 88%, sensitivity equal to 89% and specificity equal to 86%. In addition to the minute-by-minute classification, the results showed that the two classifiers are able to separate entirely (100%) the normal recordings from the apneic recordings. Finally, an additional database with eight recordings annotated as normal or apneic was used to test again the classifiers. Also in this new dataset, the results showed a complete separation between apneic and normal recordings.

A-phases are transient events with a length between 2 and 60 s that can be observed in the EEG during NREM sleep. Recent studies have characterized the properties of the A-phases based on the spectral components of the EEG signal. These studies have given interesting insight about the Aphases and have helped to develop automatic algorithms for their detection. The characterization includes the evaluation of only the typical bands of the EEG signal, including Delta (0.03 Hz - 4 Hz), Theta (4 Hz - 8 Hz), Alpha (8 Hz - 12 Hz) and Beta (12 Hz - 30 Hz); however, to our knowledge, there are not any sleep EEG studies during the A-phases involving frequencies higher than the Beta band (e.g., the Gamma band (30 Hz - 100 Hz)). This paper focuses on the characterization of the spectral components of the A-phases on EEG activity by means of Principal Component Analysis, considering the full bandwidth of the EEG signal (up to 100 Hz). The results showed that frequency components above 50 Hz exist, especially during the A3-phase. Thus the results suggest that Gamma activity can be found during the NREM sleep process.

The sleep phenomenon is a complex process that involves fluctuations of autonomic functions such as the blood pressure, temperature and brain function. These fluctuations change their properties through the different sleep stages with specific relations among the different systems. In order to understand the relation between the cardiovascular and central nervous system at the different sleep stages, we applied different non-linear methods to the energy of electroencephalographic signal (EEG) and the heart rate fluctuations. The EEG was divided in the Delta, Theta, Alpha and Beta frequency bands and the mean energy of these bands was computed at each heart rate interval. Thus, the non-linear relation was evaluated between the energy of the EEG bands and the heart rate fluctuations using Cross-Correlation, Cross-Sample Entropy and Recurrence Quantification Analysis in segments of 5 minutes grouped by sleep stage. The results showed that a relation exists between the changes of the energy in the Delta band and the Heart rate fluctuations.

Insomnia is a condition that affects the nervous and muscular system. Thirty percent of the population between 18 and 60 years suffers from insomnia. The effects of this disorder involve problems such as poor school or job performance and traffic accidents. In addition, patients with insomnia present changes in the cardiac function during sleep. Furthermore, the structure of electroencephalographic A-phases, which builds up the Cyclic Alternating Pattern during sleep, is related to the insomnia events. Therefore, the relationship between these brain activations (A-phases) and the autonomic nervous system would be of interest, revealing the interplay of central and autonomic activity during insomnia. With this goal, a study of the relationship between A-phases and heart rate fluctuations is presented. Polysomnography recording of five healthy subjects, five sleep misperception patients and five patients with psychophysiological insomnia were used in the study. Detrended Fluctuation Analysis (DFA) was used in order to evaluate the heart rate dynamics and this was correlated with the number of A-phases. The results suggest that pathological patients present changes in the dynamics of the heart rate. This is reflected in the modification of A-phases dynamics, which seems to modify of heart rate dynamics.

Evaluation of the RR variability was carried out during the Cyclic Alternating Pattern (CAP) in sleep. CAP is a central phenomenon formed by short events called A-phases that break basal electroencephalogram (EEG) oscillations of the sleep stages. A-phases are classified in three types (A1, A2 and A3) based on the EEG desynchronization during A-phase. However, the relation of A-phases with other systems, such as cardiovascular system, is unclear and a deep analysis is required. For the study, six patients with Nocturnal Front Lobe Epilepsy (NFLE) and other six healthy controls patients underwent whole night polysomnographic recordings with CAP and hypnogram annotations. Amplitude reduction and time delay of the RR intervals minimum with respect to A-phases onset were computed. In addition, the same process was computed over randomly chosen RR interval segments during the NREM sleep for further comparison. The results suggest that the onset of the A-phases is correlated with a significative increase of the heart rate that peaks at around 4s after the Aphase onset, independently of the A-phase subtype.

—A statistical analysis of theseparabilityof EEG A-phases, with respect to basal activity, is presented in this study. the brain activityA-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around the onset and offset of the A-phases were used to evaluate the separability between A-phases and basal sleep stage oscillations. In addition, a classifier was trained to separate the different A-phase types (A1, A2 and A3). Temporal, energy and complexity measures were used as descriptors for the classifier. The results show a percentage of separation between onset and preceeding basal oscillations higher than 85 % for all A-phases types. For Offset separation from following baseline, the accuracy is higher than 80 % but specificity is around 75%. Concerning to A-phase type separation, A1-phase and A3-phase are well separated with accuracy higher than 80, while A1 and A2-phases show a separation lower than 50%. These results encourage the design of automatic classifiers for Onset detection and for separating among A-phases type A1 and A3. On the other hand, the A-phase Offsets present a smooth transition towards the basal sleep stage oscillations, and A2-phases are very similar to A1-phases, suggesting that a high uncertainty may exist during CAP annotation.

Raman spectroscopy of biological samples presents undesirable noise and fluorescence generated by the biomolecular excitation. The reduction of these types of noise is a fundamental task to obtain the valuable information of the sample under analysis. This paper proposes the application of the empirical mode decomposition (EMD) for noise elimination. EMD is a parameter-free and adaptive signal processing method useful for the analysis of nonstationary signals. EMD performance was compared with the commonly used Vancouver algorithm (VRA) through artificial data (Teflon), synthetic (Vitamin E and paracetamol) and biological (Mouse brain and human nails) Raman spectra. The correlation coefficient ([Formula: see text]) was used as performance measure. Results on synthetic data showed a better performance of EMD ([Formula: see text]) at high noise levels compared with VRA ([Formula: see text]). The methods with simulated fluorescence added to artificial material exhibited a similar shape of fluorescence in both cases ([Formula: see text] for VRA and [Formula: see text] for EMD). For synthetic data, Raman spectra of vitamin E were used and the results showed a good performance comparing both methods ([Formula: see text] for EMD and [Formula: see text] for VRA). Finally, in biological data, EMD and VRA displayed a similar behavior ([Formula: see text] for EMD and [Formula: see text] for VRA), but with the advantage that EMD maintains small amplitude Raman peaks. The results suggest that EMD could be an effective method for denoising biological Raman spectra, EMD is able to retain information and correctly eliminates the fluorescence without parameter tuning.

Quadrature bandpass filters take a real-valued signal and output an analytic signal from which the instantaneous amplitude and phase can be computed. For this reason, they represent a useful tool to extract time-varying, narrow-band information from electrophysiological signals such as electroencephalogram (EEG) or electrocardiogram. One of the defining characteristics of quadrature filters is its null response to negative frequencies. However, when the frequency band of interest is close to 0 Hz, a careless filter design could let through negative frequencies, producing distortions in the amplitude and phase of the output. In this work, three types of quadrature filters (Ideal, Gabor and Sinusoidal) have been evaluated using both artificial and real EEG signals. For the artificial signals, the performance of each filter was measured in terms of the distortion in amplitude and phase, and sensitivity to noise and bandwidth selection. For the real EEG signals, a qualitative evaluation of the dynamics of the synchronization between two EEG channels was performed. The results suggest that, while all filters under study behave similarly under noise, they differ in terms of their sensitivity to bandwidth choice. In this study, the Sinusoidal filter showed clear advantages for the estimation of low-frequency EEG synchronization.

The aim of this work is to study the behavior of the autonomic system through variations in the heart rate (HR) during the Cyclic Alternating Pattern (CAP) which is formed by A-phases. The analysis was carried out in 10 healthy subjects and 10 patients with Nocturnal Front Lobe Epilepsy (NFLE) that underwent one whole night of polysomnographic recordings. In order to assess the relation of A-phases with the cardiovascular system, two time domain features were computed: the amplitude reduction and time delay of the minimum of the R-R intervals with respect to A-phases onset. In addition, the same process was performed over randomly chosen R-R interval segments during the NREM sleep for baseline comparisons. A non-parametric bootstrap procedure was used to test differences of the kurtosis values of two populations. The results suggest that the onset of the A-phases is correlated with a significant increase of the HR that peaks at around 4[Formula: see text]s after the A-phase onset, independently of the A-phase subtype and sleep time for both healthy subjects and NFLE patients. Furthermore, the behavior of the reduction in the R-R intervals during the A-phases was significantly different for NFLE patients with respect to control subjects.

This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear mixture model with positivity and sum-to-one restrictions on the abundances and end-members to compensate for signature variability. The synthesis procedure depends on a quadratic optimization problem, which is solved by an alternating least-squares structure over convex sets. The BEAE strategy only assumes that the number of components in the analyzed sample is known a spriori. The proposed method is first validated by using synthetic m-FLIM datasets at 15, 20, and 25 dB signal-to-noise ratios. The samples simulate the mixed response of tissue containing multiple fluorescent intensity decays. Furthermore, the results were also validated with six m-FLIM datasets from fresh postmortem human coronary atherosclerotic plaques. A quantitative evaluation of the BEAE was made against two popular techniques: minimum volume constrained nonnegative matrix factorization (MVC-NMF) and multivariate curve resolution-alternating least-squares (MCR-ALS). Our proposed method (BEAE) was able to provide more accurate estimations of the end-members: 0.32% minimum relative error and 13.82% worst-case scenario, despite different initial conditions in the iterative optimization procedure and noise effect. Meanwhile, MVC-NMF and MCR-ALS presented more variability in estimating the end-members: 0.35% and 0.34% for minimum errors and 15.31% and 13.25% in the worst-case scenarios, respectively. This tendency was also maintained for the abundances, where BEAE obtained 0.05 as the minimum absolute error and 0.12 in the worst-case scenario; MCR-ALS and MVC-NMF achieved 0.04 and 0.06 for the minimum absolute errors, and 0.15 and 0.17 under the worst-case conditions, respectively. In addition, the average computation time was evaluated for the synthetic datasets, where MVC-NMF achieved the fastest time, followed by BEAE and finally MCR-ALS. Consequently, BEAE improved MVC-NMF in convergence to a local optimal solution and robustness against signal variability, and it is roughly 3.6 time faster than MCR-ALS.

A-phases consist of transient cortical events that normally occur during NREM sleep and can be observed directly in the EEG signals. One particular kind of A-phases, namely, A3-phases are related to arousals from sleep during which increased activity in other systems (such as the cardiovascular and respiratory systems) can also be observed. This study aims to characterize disruptions in the oscillations of the airflow signal during A3-phases of sleep. Spectral entropy was used to quantify the bandwidth of the airflow signal, which under baseline conditions (prior to an A3-phase) resembles a sinusoidal wave with a frequency of about 0.25 Hz and has low spectral entropy values. It was found that during most A3-phases the spectral entropy increases significantly in 70% of the test subjects. These changes occur with higher probability during A3-phases that are longer than 10[Formula: see text]s, suggesting a delay between the onset of an A3-phase and the effect it has on the respiratory system.

A Pressure Bed Sensor (PBS) can offer an unobtrusive method for sleep monitoring. This study focuses on the detection of the sleep related breathing disorders using a PBS in comparison to the methods used in a sleep laboratory. A newly developed PCA modeling approach for the eight sensor signals of the PBS is evaluated using the Reduced Respiratory Amplitude Index (RRAI) as a central measure. The method computes the respiration amplitude with the Hilbert transform, and then detects the events based on a 20% amplitude reduction from the baseline signal. A similar calculation was used for the sleep laboratory RIP measurements, and both PBS and RIP were compared against the reference based on the nasal flow signal. In the reference RRAI method, the respiratory-disordered events were obtained using RemLogic respiration analyzer to detect over 50% amplitude reduction in the nasal respiratory flow, but removing the RemLogic standard hypopnea event associations on the oxygen desaturation events and the sleep arousals. The movement artifacts were automatically detected based on the movement activity signal of the PBS. Twenty-five (25) out of 28 patients were finally analysed. On average 87% of a night measurement has been covered by the system. The correlation coefficient was 0.92 between the PBS and the reference RRAI, and the performance of the PBS was similar with the RIP belts. Classifying the severity of the sleep related breathing by dividing RRAI in groups according to the severity criteria, the sensitivity was 92% and the specificity was 70% for the PBS. The results suggest that PBS recording can provide an easy and un-obstructive alternative method for the detection of the sleep disordered breathing and thus has a great promise for the home monitoring.

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

article i nfo Background:Treatment with positive airwaypressuredevicesimproved signs andsymptomsof obstructive sleep apnea syndrome (OSA); however, auto-adjusting positive pressure (APAP) device was not as effective as contin- uous positive airway pressure (CPAP) in reducing arterial blood pressure and insulin resistance. The role played by autonomic cardiac regulation remains to be clarified. We aimed to test the effects of CPAP and APAP on autonomic regulation and cardiorespiratory coupling during sleep. Methods: We retrospectively analyzed full-night polysomnographic studies. 19 patients newly diagnosed with severe OSA (AHI N 30) and 7 obese subjects without OSA (CON) were enrolled. Each OSA subject was assigned to CPAP or APAP treatment and underwent a sleep study after 1 week of treatment. Spectral and cross-spectral analyses of heart rate variability (HRV) and respiration were performed to assess autonomic profile and coherence (K 2 ) between respiration and HF oscillation during sleep in CPAP, APAP and CON groups. Results: In CPAP and CON, LFnu and LF/HF, markers of sympathetic modulation, decreased from N2 to N3 and increased during REM sleep (p b 0.001), while in APAP group, sympathetic modulation was significantly higher compared with those of CPAP and CON during all sleep stages. K 2 values were lower in APAP compared with those in CPAP and CON. Conclusion: APAP treatment was characterized by a greater sympathetic activation and it was associated with a lower cardio-respiratory coupling compared with CPAP. This might account for the different effects on cardiovascu- lar risk factors induced by the two treatments.

This work is part of a continuous effort to achieve characterization of tissue from auto-fluorescence measurements. One particular problem is the estimation of the number of components in a sample from multi-spectral Fluorescence Lifetime Imaging Data (m-FLIM). The proposed method is based on a two-step iterative procedure, where first a blind end-member and abundance extraction algorithm is employed, followed by an evaluation of the resulting end-members by solving an optimal approximation problem. A threshold method is employed to evaluate if the extracted end-members are nonredundant. The validation of the proposal is performed by 3 m-FLIM data sets from post-mortem human coronary artery samples, where the results obtained matched the qualitative description provided by histopathology slides.