Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P¿=¿0.780) and the extended modified beta distribution (P¿=¿0.765) provided similar results, higher than the rest of analyzed kernels.Peer ReviewedPostprint (published version

Comparison of two cardiac output monitors, qCO and LiDCO, during general anesthesia

Background: Optimization of cardiac output (CO) has been evidenced to reduce postoperative complications and to expedite the recovery. Likewise, CO and other dynamic cardiac parameters can describe the systemic blood flow and tissue oxygenation state and can be useful in different clinical fields. This study aimed to validate the qCO monitor (Quantium Medical, Barcelona, Spain), a new device to estimate CO and other related parameters in a continuous, fully non-invasive way using advanced digital signal processing of impedance cardiography. Methods: The LiDCOrapidv2 (LiDCO Ltd, London, UK) was used to compare the performance of the qCO in 15 patients during major surgery under general anesthesia. Full surgeries were recorded and cardiac output obtained by both devices was compared by using correlation and Bland-Altman analysis. Results: The Bland-Altman analysis showed sufficient agreement with a mean bias of -0.03 ± 0.71 L/min. Conclusions: The findings showed that both systems offered comparable values and thus the non-invasive measurement of CO with qCO is a promising, feasible method. Further investigation will be required to validate this new device against calibrated devices and outcome studies would also be highly recommended.Postprint (author's final draft

Characterization of the autonomic nervous system response under emotional stimuli through linear and non-linear analysis of physiological signals

En esta disertación se presentan metodologías lineales y no lineales aplicadas a señales fisiológicas, con el propósito de caracterizar la respuesta del sistema nervioso autónomo bajo estímulos emocionales. Este estudio está motivado por la necesidad de desarrollar una herramienta que identifique emociones en función de su efecto sobre la actividad cardíaca, ya que puede tener un impacto potencial en la práctica clínica para diagnosticar enfermedades psico-neuronales.Las hipótesis de esta tesis doctoral son que las emociones inducen cambios notables en el sistema nervioso autónomo y que estos cambios pueden capturarse a partir del análisis de señales fisiológicas, en particular, del análisis conjunto de la variabilidad del ritmo cardíaco (HRV) y la respiración.La base de datos analizada contiene el registro simultáneo del electrocardiograma y la respiración de 25 sujetos elicitados con emociones inducidas por vídeos, incluyendo las siguientes emociones: alegría, miedo, tristeza e ira.En esta disertación se describen dos estudios metodológicos.En el primer estudio se propone un método basado en el análisis lineal de la HRV guiado por la respiración. El método se basó en la redefinición de la banda de alta frecuencia (HF), no solo centrándose en la frecuencia respiratoria, sino también considerando un ancho de banda que dependiera del espectro respiratorio. Primero, el método se validó con señales de HRV simuladas, obteniéndose errores mínimos de estimación en comparación con la definición de la banda de HF clásica e incluso con la banda de HF centrada en la frecuencia respiratoria pero con un ancho de banda constante, independientemente de los valores del ratio simpático-vagal.Después, el método propuesto se aplicó en una base de datos de elicitación emocional inducida mediante vídeos para discriminar entre emociones. No solo la banda de HF redefinida propuesta superó a las otras definiciones de banda de HF en discriminación emocional, sino también la correlación máxima entre los espectros de la HRV y de la respiración discriminó alegría y relajación, alegría y cada emoción de valencia negativa y entre miedo y tristeza con un p-valor ≤ 0.05 y AUC ≥ 0.70.En el segundo estudio, técnicas no lineales como la Función de Auto Información Mutua y la Función de Información Mutua Cruzada, AMIF y CMIF respectivamente, son también propuestas en esta tesis doctoral para el reconocimiento de emociones humanas. La técnica AMIF se aplicó a las señales de HRV para estudiar interdependencias complejas, y se consideró la técnica CMIF para cuantificar el acoplamiento complejo entre las señales de HRV y de respiración. Ambos algoritmos se adaptaron a las series temporales RR de corta duración. Las series RR fueron filtradas en las bandas de baja y alta frecuencia, y también se investigaron las series RR filtradas en un ancho de banda basado en la respiración.Los resultados revelaron que la técnica AMIF aplicada a la serie temporal RR filtrada en la banda de HF redefinida fue capaz de discriminar entre: relajación y alegría y miedo, alegría y cada valencia negativa y finalmente miedo y tristeza e ira, todos con un nivel de significación estadística (p-valor ≤ 0.05, AUC ≥ 0.70). Además, los parámetros derivados de AMIF y CMIF permitieron caracterizar la baja complejidad que la señal presentaba durante el miedo frente a cualquier otro estado emocional estudiado.Finalmente se investiga, mediante un clasificador lineal, las características lineales y no lineales que discriminan entre pares de emociones y entre valencias emocionales para determinar qué parámetros permiten diferenciar los grupos y cuántos de éstos son necesarios para lograr la mejor clasificación posible. Los resultados extraídos de este capítulo sugieren que pueden ser clasificadas mediante el análisis de la HRV: relajación y alegría, la valencia positiva frente a todas las negativas, alegría y miedo, alegría y tristeza, alegría e ira, y miedo y tristeza.El análisis conjunto de la HRV y la respiración aumenta la capacidad discriminatoria de la HRV, siendo la máxima correlación entre los espectros de la HRV y la respiración uno de los mejores índices para la discriminación de emociones. El análisis de la información mutua, aun en señales de corta duración, añade información relevante a los índices lineales para la discriminación de emociones.<br /

Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels

Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.Peer ReviewedPostprint (author's final draft

Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection

Objective: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers. Methods:A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (t). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection. Results:Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R. Conclusions Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changesPeer ReviewedPostprint (published version

Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.Peer ReviewedPostprint (published version

Measuring instantaneous and spectral information entropies by shannon entropy of choi-williams distribution in the context of electroencephalography

The theory of Shannon entropy was applied to the Choi-Williams time-frequency distribution (CWD) of time series in order to extract entropy information in both time and frequency domains. In this way, four novel indexes were defined: (1) partial instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function at each time instant taken independently; (2) partial spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of each frequency value taken independently; (3) complete instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function of the entire CWD; (4) complete spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of the entire CWD. These indexes were tested on synthetic time series with different behavior (periodic, chaotic and random) and on a dataset of electroencephalographic (EEG) signals recorded in different states (eyes-open, eyes-closed, ictal and non-ictal activity). The results have shown that the values of these indexes tend to decrease, with different proportion, when the behavior of the synthetic signals evolved from chaos or randomness to periodicity. Statistical differences (p-value < 0.0005) were found between values of these measures comparing eyes-open and eyes-closed states and between ictal and non-ictal states in the traditional EEG frequency bands. Finally, this paper has demonstrated that the proposed measures can be useful tools to quantify the different periodic, chaotic and random components in EEG signals. © 2014 by the authors; licensee MDPI, Basel, Switzerland.Peer ReviewedPostprint (published version

Mutual information between heart rate variability and respiration for emotion characterization

Objective: Interest in emotion recognition has increased in recent years as a useful tool for diagnosing psycho-neural illnesses. In this study, the auto-mutual and the cross-mutual information function, AMIF and CMIF respectively, are used for human emotion recognition. Approach: The AMIF technique was applied to heart rate variability (HRV) signals to study complex interdependencies, and the CMIF technique was considered to quantify the complex coupling between HRV and respiratory signals. Both algorithms were adapted to short-term RR time series. Traditional band pass filtering was applied to the RR series at low frequency (LF) and high frequency (HF) bands, and a respiration-based filter bandwidth was also investigated (). Both the AMIF and the CMIF algorithms were calculated with regard to different time scales as specific complexity measures. The ability of the parameters derived from the AMIF and the CMIF to discriminate emotions was evaluated on a database of video-induced emotion elicitation. Five elicited states i.e. relax (neutral), joy (positive valence), as well as fear, sadness and anger (negative valences) were considered. Main results: The results revealed that the AMIF applied to the RR time series filtered in the band was able to discriminate between the following: relax and joy and fear, joy and each negative valence conditions, and finally fear and sadness and anger, all with a statistical significance level p¿-value 0.05, sensitivity, specificity and accuracy higher than 70% and area under the receiver operating characteristic curve index AUC 0.70. Furthermore, the parameters derived from the AMIF and the CMIF allowed the low signal complexity presented during fear to be characterized in front of any of the studied elicited states. Significance: Based on these results, human emotion manifested in the HRV and respiratory signal responses could be characterized by means of the information-content complexityPeer ReviewedPostprint (author's final draft

Comparison of the qCON and qNOX indices for the assessment of unconsciousness level and noxious stimulation response during surgery

The objective of this work is to compare the performances of two electroencephalogram based indices for detecting loss of consciousness and loss of response to nociceptive stimulation. Specifically, their behaviour after drug induction and during recovery of consciousness was pointed out. Data was recorded from 140 patients scheduled for general anaesthesia with a combination of propofol and remifentanil. The qCON 2000 monitor (Quantium Medical, Barcelona, Spain) was used to calculate the qCON and qNOX. Loss of response to verbal command and loss of eye-lash reflex were assessed during the transition from awake to anesthetized, defining the state of loss of consciousness. Movement as a response to laryngeal mask (LMA) insertion was interpreted as the response to the nociceptive stimuli. The patients were classified as movers or non-movers. The values of qCON and qNOX were statistically compared. Their fall times and rise times defined at the start and at the end of the surgery were calculated and compared. The results showed that the qCON was able to predict loss of consciousness such as loss of verbal command and eyelash reflex better than qNOX, while the qNOX has a better predictive value for response to noxious stimulation such as LMA insertion. From the analysis of the fall and rise times, it was found that the qNOX fall time (median: 217 s) was significantly longer (p value <0.05) than the qCON fall time (median: 150 s). At the end of the surgery, the qNOX started to increase in median at 45 s before the first annotation related to response to stimuli or recovery of consciousness, while the qCON at 88 s after the first annotation related to response to stimuli or recovery of consciousness (p value <0.05). The indices qCON and qNOX showed different performances in the detection of loss of consciousness and loss of response to stimuli during induction and recovery of consciousness. Furthermore, the qCON showed faster decrease during induction. This behaviour is associated with the hypothesis that the loss of response to stimuli (analgesic effect) might be reached after the loss of consciousness (hypnotic effect). On the contrary, the qNOX showed a faster increase at the end of the surgery, associated with the hypothesis that a higher probability of response to stimuli might be reached before the recovery of consciousness.Postprint (author's final draft

Mutual information measures applied to EEG signals for sleepiness characterization

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in ß band during MSLT events (. p-value<0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients.Peer ReviewedPostprint (author's final draft