Psicólogos en formación, modelos mentales y sistemas de actividad inter-institucionales: cambio cognitivo y aprendizaje por expansión / Psychologists in modelling, mental models and inter-institutional systems of activity: cognitive change and expansive learning

El trabajo analiza figuras del psicólogo de Estudiantes de Psicología de Universidad de Buenos Aires, antes y después de las Prácticas Profesionales y de Investigación, en 2008 y 2009. El objetivo es comprender la heterogeneidad de experiencias y los cambios cognitivos y actitudinales a través de los giros en los modelos mentales de los psicólogos en formación. La participación guiada en comunidades de práctica y la implicación en procesos de internalización y externalización son fundamentales para la apropiación de instrumentos de mediación y la construcción de competencias profesionales. El análisis factorial evidencia confiabilidad de los instrumentos de recolección de datos e identifica sistemas representacionales. Se denotan fortalezas y desafíos de la profesionalización y enriquecimiento en ejes relevantes: perspectivismo, tramas intersubjetivas y psicosociales, problemas y acciones complejas, inter.-agencialidad, especificidad de la Psicología y articulación con otras disciplinas; objetivos, formulación de hipótesis, direccionalidad de intervenciones, multiplicidad de herramientas, resultados y atribuciones.

The study analyses the figures of psychologists that students of Psychology at Buenos Aires University have built, at the beginning and at the end of Undergraduate Professional and Research Apprenticeship, in 2008 and 2009. The aim is to appreciate the heterogeneity of experiences and the change of cognitions and attitudes through the shifts in mental models of future psychologists in training. Guided participation in communities of practice and the involvement in processes of internalization and externalization are important for the appropriation of mediating instruments and for the construction of professional competences. The factorial analysis demonstrates confiability of instruments of data collection and identifies representational systems. Strengths and challenges are embedded in the process of becoming professional psychologist. Data show enhancement in significant axes: perspectivism, interpersonal and psycho-social wefts, complex problems and activities, inter-agencies, specificity of Psychology and its joint with other sciences, aims, hypothesis, direction of interventions, multiplicity of tools, results and attributions.

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.

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.

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).

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.

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.

Comparison of a Sleep Quality Index between Normal and Obstructive Sleep Apnea Patients.

This paper aimed at developing an index, called Sleep Index, to assess the quality of sleep in normal and obstructive sleep apnea (OSA) subjects. The Sleep Index was designed to be directly proportional to the summation of product of the number seconds spent in each stage of sleep and the selected weighting coefficient of each sleep stage. It was also inversely proportional to the product of total number of sleep stage shifts and the total number of seconds spent in all stages of sleep. In order to test the proposed index, data from eleven previously diagnosed sleep apnea subjects (6 females and 5 males; Age: 50 ± 8.94; Body Mass Index (BMI): 31.70 ± 6.97) and 14 normal subjects (8 females and 6 males; Age: 46.43 ± 9.61; BMI: 25.541± 3.061) were used. All the subjects underwent nocturnal polysomnography (NPSG) at an accredited sleep center. Statistical testing of the Sleep Index showed that its mean was significantly different for normal and OSA subjects (p<0.04). The Sleep Index values were higher for normal subjects than for sleep apnea subjects. This was in part due to the higher number of sleep stage shifts in OSA subjects compared to normal subjects. Therefore, higher Sleep Index values reflect better sleep quality. The number of sleep stage shifts should be higher for sleep apnea subjects as they frequently experience sleep arousal and sleep continuity is impaired, resulting in daytime sleepiness.

Spectral analysis of R wave attenuation and heart rate variability for detection of cheyne stokes breathing.

Spectral analysis was carried out on the R-Wave Attenuation (RWA) trend and Heart Rate Variability (HRV) series, derived from the polysomnographic Electrocardiogram (ECG) of the subjects with and without Cheyne Stokes Breathing. Nocturnal polysomnography was performed on 16 Normal subjects and 7 subjects with Cheyne Stokes Breathing (CSB) patients. The polysomnographic ECG data was divided into fifteen minute epochs for analysis. These epochs are processed to obtain the RWA. Hilbert Transform based algorithm [4] was used for QRS detection. Power spectrum of RWA and HRV are computed for each clip by using Welch's averaged periodogram method. HRV is sensitive to REM sleep as well and hence not specific to sleep apnea [12]. Hence the parameters derived from HRV alone cannot be used as diagnostic markers. Hence a combined detection scheme which uses parameters derived from RWA and HRV power spectrum is used in the proposed method to increase detection accuracy. This method produced a sensitivity of 84.75% and specificity of 87.03% in the training set and sensitivity of 85.78% and a specificity of 87.19% 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.