Real-time algorithm for changes detection in depth of anesthesia signals

This paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A Page-Hinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their "age" so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a two-fold approach. First, the algorithm was run offline in depth of anesthesia (DoA) signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69 % of the changes were classified as true positives, 26 % as false positives, and 5 % as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice. © 2012 Springer-Verlag

Chaos-modified detrended moving average methodology for monitoring the depth of anaesthesia

This paper proposes a new method to monitor the depth of anaesthesia (DoA) based on the EEG signal. This approach firstly uses discrete wavelet transform (DWT) to to remove the spikes and the low frequency noise from raw EEG signals. After de-noising the EEG signals, the modified Hurst parameter is proposed with two new indices (CDoA and CsDoA), to estimate the anaesthesia states of the patients. To reduce the fluctuation of the new DoA index, a combination of Modified Chaos and Modifying Detrended Moving Average is used (MC-DMA). Analyses of variance (ANOVA) for C-MDMA and BIS distributions are presented The results indicate that the C-MDMA distributions at each anaesthesia state level are significantly different and the C-MDMA can distinguish five depths of anaesthesia. Compared with BIS trends, MC-DMA trend is close to BIS trend covering the whole scale from 100 to 0 with a full recording time

Pain detection with bioimpedance methodology from 3-dimensional exploration of nociception in a postoperative observational trial

Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0-10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r(2) = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions

An assessment of pulse transit time for detecting heavy blood loss during surgical operation

Copyright @ Wang et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.The main contribution of this paper is the use of non-invasive measurements such as electrocardiogram (ECG) and photoplethysmographic (PPG) pulse oximetry waveforms to develop a new physiological signal analysis technique for detecting blood loss during surgical operation. Urological surgery cases were considered as the control group due to its generality, and cardiac surgery as experimental group since it involves blood loss and water supply. Results show that the control group has the tendency of a reduction of the pulse transient time (PTT), and this indicates an increment in the blood flow velocity changes from slow to fast. While for the experimental group, the PTT indicates high values during blood loss, and low values during water supply. Statistical analysis shows considerable differences (i.e., P <0.05) between both groups leading to the conclusion that PTT could be a good indicator for monitoring patients' blood loss during a surgical operation.The National Science Council (NSC) of Taiwan and the Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan

A novel multivariate STeady-state index during general ANesthesia (STAN)

The assessment of the adequacy of general anesthesia for surgery, namely the nociception/anti-nociception balance, has received wide attention from the scientific community. Monitoring systems based on the frontal EEG/EMG, or autonomic state reactions (e.g. heart rate and blood pressure) have been developed aiming to objectively assess this balance. In this study a new multivariate indicator of patients' steady-state during anesthesia (STAN) is proposed, based on wavelet analysis of signals linked to noxious activation. A clinical protocol was designed to analyze precise noxious stimuli (laryngoscopy/intubation, tetanic, and incision), under three different analgesic doses; patients were randomized to receive either remifentanil 2.0, 3.0 or 4.0 ng/ml. ECG, PPG, BP, BIS, EMG and [Formula: see text] were continuously recorded. ECG, PPG and BP were processed to extract beat-to-beat information, and [Formula: see text] curve used to estimate the respiration rate. A combined steady-state index based on wavelet analysis of these variables, was applied and compared between the three study groups and stimuli (Wilcoxon signed ranks, Kruskal-Wallis and Mann-Whitney tests). Following institutional approval and signing the informed consent thirty four patients were enrolled in this study (3 excluded due to signal loss during data collection). The BIS index of the EEG, frontal EMG, heart rate, BP, and PPG wave amplitude changed in response to different noxious stimuli. Laryngoscopy/intubation was the stimulus with the more pronounced response [Formula: see text]. These variables were used in the construction of the combined index STAN; STAN responded adequately to noxious stimuli, with a more pronounced response to laryngoscopy/intubation (18.5-43.1 %, [Formula: see text]), and the attenuation provided by the analgesic, detecting steady-state periods in the different physiological signals analyzed (approximately 50 % of the total study time). A new multivariate approach for the assessment of the patient steady-state during general anesthesia was developed. The proposed wavelet based multivariate index responds adequately to different noxious stimuli, and attenuation provided by the analgesic in a dose-dependent manner for each stimulus analyzed in this study.The first author was supported by a scholarship from the Portuguese Foundation for Science and Technology (FCT SFRH/BD/35879/2007). The authors would also like to acknowledge the support of UISPA—System Integration and Process Automation Unit—Part of the LAETA (Associated Laboratory of Energy, Transports and Aeronautics) a I&D Unit of the Foundation for Science and Technology (FCT), Portugal. FCT support under project PEst-OE/EME/LA0022/2013.info:eu-repo/semantics/publishedVersio

Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection

Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p  0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen’s kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring. GRAPHICAL ABSTRACT: [Image: see text

Advanced bioimpedance signal processing techniques for hemodynamic monitoring during anesthesia

Aplicat embargament des de la data de defensa fins els maig 2020.Cardiac output (CO) defines the blood flow arriving from the heart to the different organs in the body and it is thus a primary determinant of global 02 transport. Cardiac output has traditionally been measured using invasive methods, whose risk sometimes exceeds the advantages of a cardiac output monitoring. In this context, the minimization of risk in new noninvasive technologies for CO monitoring could translate into major advantages for clinicians, hospitals and patients: ease of usage and availability, reduced recovery time, and improved patient outcome. Impedance Cardiography (ICG) is a promising noninvasive technology for cardiac output monitoring but available information on the ICG signals is more scare than other physiological signals such as the electrocardiogram (ECG). The present Doctoral Thesis contributes to the development of signal treatment techniques for the ICG in order to create an innovative hemodynamic monitor. First, an extensive literature review is provided regarding the basics of the clinical background in which cardiac output monitoring is used and concerning the state of the art of cardiac output monitors on the market. This Doctoral Thesis has produced a considerable amount of clinical data which is also explained in detail. These clinical data are also useful to complement the theoretical explanation of patient indices such as heart rate variability, blood flow and blood pressure. In addition, a new method to create synthetic biomedical signals with known time-frequency characteristics is introduced. One of the first analysis in this Doctoral Thesis studies the time difference between peak points of the heart beats in the ECG and the ICG: the RC segment. This RC segment is a measure of the time delay between electrical and mechanical activity of the heart. The relationship of the RC segment with blood pressure and heart interval is analyzed. The concordance of beat durations of both the electrocardiogram and the impedance cardiogram is one of the key results to develop new artefact detection algorithms and the RC could also have an impact in describing the hemodynamics of a patient. Time-frequency distributions (TFDs) are also used to characterize how the frequency content in impedance cardiography signals change with time. Since TFDs are calculated using concrete kernels, a new method to select the best kernel by using synthetic signals is presented. Optimized TFDs of ICG signals are then calculated to extract severa! features which are used to discriminate between different anesthesia states in patients undergoing surgery. TFD-derived features are also used to describe the whole surgical operations. Relationships between TFD-derived features are analyzed and prediction models for cardiac output are designed. These prediction models prove that the TFD-derived features are related to the patients' cardiac output. Finally, a validation study for the qCO monitor is presented. The qCO monitor has been designed using sorne of the techniques which are consequence of this Doctoral Thesis. The main outputs of this work have been protected with a patent which has already been filed. As a conclusion, this Doctoral Thesis has produced a considerable amount of clinical data and a variety of analysis and processing techniques of impedance cardiography signals which have been included into commercial medical devices already available on the market.El gasto cardíaco (GC) define el flujo de sangre que llega desde el corazón a los distintos órganos del cuerpo y es, por tanto, un determinante primario del transporte global de oxígeno. Se ha medido tradicionalmente usando métodos invasivos cuyos riesgos excedían en ocasiones las ventajas de su monitorización. En este contexto, la minimización del riesgo de la monitorización del gasto cardíaco en nuevas tecnologías no invasivas podría traducirse en mayores ventajas para médicos, hospitales y pacientes: facilidad de uso, disponibilidad del equipamiento y menor tiempo de recuperación y mejores resultados en el paciente. La impedancio-cardiografía o cardiografía de impedancia (ICG} es una prometedora tecnología no invasiva para la monitorización del gasto cardíaco. Sin embargo, la información disponible sobre las señales de ICG es más escasa que otras señales fisiológicas como el electrocardiograma (ECG). La presente Tesis Doctoral contribuye al desarrollo de técnicas de tratamiento de señal de ICG para así crear un monitor hemodinámico innovador. En primer lugar, se proporciona una extensa revisión bibliográfica sobre los aspectos básicos del contexto clínico en el que se utiliza la monitorización del gasto cardíaco así como sobre el estado del arte de los monitores de gasto cardíaco que existen en el mercado. Esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos que también se explican en detalle. Dichos datos clínicos también son útiles para complementar las explicaciones teóricas de los índices de paciente de variabilidad cardíaca y el flujo y la presión sanguíneos. Además, se presenta un nuevo método de creación de señales sintéticas biomédicas con características de tiempo-frecuencia conocidas. Uno de los primeros análisis de esta Tesis Doctoral estudia la diferencia temporal entre los picos de los latidos cardíacos del ECG y del ICG: el segmento RC. Este segmento RC es una medida del retardo temporal entre la actividad eléctrica y mecánica del corazón. Se analiza la relación del segmento RC con la presión arterial y el intervalo cardíaco. La concordancia entre la duración de los latidos del ECG y del ICG es uno de los resultados claves para desarrollar nuevos algoritmos de detección de artefactos y el segmento RC también podría ser relevante en la descripción de la hemodinámica de los pacientes. Las distribuciones de tiempo-frecuencia (TFD, por sus siglas en inglés) se utilizan para caracterizar cómo el contenido de las señales de impedancia cardiográfica cambia con el tiempo. Dado que las TFDs deben calcularse usando núcleos (kernels, en inglés) concretos, se presenta un nuevo método para seleccionar el mejor núcleo mediante el uso de señales sintéticas. Las TFDs de ICG optimizadas se calculan para extraer distintas características que son usadas para discriminar entre los diferentes estados de anestesia en pacientes sometidos a procesos quirúrgicos. Las características derivadas de las distribuciones de tiempo-frecuencia también son utilizadas para describir las operaciones quirúrgicas durante toda su extensión temporal. La relación entre dichas características son analizadas y se proponen distintos modelos de predicción para el gasto cardíaco. Estos modelos de predicción demuestran que las características derivadas de las distribuciones tiempo-frecuencia de señales de ICG están relacionadas con el gasto cardíaco de los pacientes. Finalmente, se presenta un estudio de validación del monitor qCO, diseñado con alguna de las técnicas que son consecuencia de esta Tesis Doctoral. Las principales conclusiones de este trabajo han sido protegidas con una patente que ya ha sido registrada. Como conclusión, esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos y una variedad de técnicas de procesado y análisis de señales de cardiografía de impedancia que han sido incluidas en dispositivos biomédicos disponibles en el mercadoPostprint (published version

Advanced bioimpedance signal processing techniques for hemodynamic monitoring during anesthesia

Cardiac output (CO) defines the blood flow arriving from the heart to the different organs in the body and it is thus a primary determinant of global 02 transport. Cardiac output has traditionally been measured using invasive methods, whose risk sometimes exceeds the advantages of a cardiac output monitoring. In this context, the minimization of risk in new noninvasive technologies for CO monitoring could translate into major advantages for clinicians, hospitals and patients: ease of usage and availability, reduced recovery time, and improved patient outcome. Impedance Cardiography (ICG) is a promising noninvasive technology for cardiac output monitoring but available information on the ICG signals is more scare than other physiological signals such as the electrocardiogram (ECG). The present Doctoral Thesis contributes to the development of signal treatment techniques for the ICG in order to create an innovative hemodynamic monitor. First, an extensive literature review is provided regarding the basics of the clinical background in which cardiac output monitoring is used and concerning the state of the art of cardiac output monitors on the market. This Doctoral Thesis has produced a considerable amount of clinical data which is also explained in detail. These clinical data are also useful to complement the theoretical explanation of patient indices such as heart rate variability, blood flow and blood pressure. In addition, a new method to create synthetic biomedical signals with known time-frequency characteristics is introduced. One of the first analysis in this Doctoral Thesis studies the time difference between peak points of the heart beats in the ECG and the ICG: the RC segment. This RC segment is a measure of the time delay between electrical and mechanical activity of the heart. The relationship of the RC segment with blood pressure and heart interval is analyzed. The concordance of beat durations of both the electrocardiogram and the impedance cardiogram is one of the key results to develop new artefact detection algorithms and the RC could also have an impact in describing the hemodynamics of a patient. Time-frequency distributions (TFDs) are also used to characterize how the frequency content in impedance cardiography signals change with time. Since TFDs are calculated using concrete kernels, a new method to select the best kernel by using synthetic signals is presented. Optimized TFDs of ICG signals are then calculated to extract severa! features which are used to discriminate between different anesthesia states in patients undergoing surgery. TFD-derived features are also used to describe the whole surgical operations. Relationships between TFD-derived features are analyzed and prediction models for cardiac output are designed. These prediction models prove that the TFD-derived features are related to the patients' cardiac output. Finally, a validation study for the qCO monitor is presented. The qCO monitor has been designed using sorne of the techniques which are consequence of this Doctoral Thesis. The main outputs of this work have been protected with a patent which has already been filed. As a conclusion, this Doctoral Thesis has produced a considerable amount of clinical data and a variety of analysis and processing techniques of impedance cardiography signals which have been included into commercial medical devices already available on the market.El gasto cardíaco (GC) define el flujo de sangre que llega desde el corazón a los distintos órganos del cuerpo y es, por tanto, un determinante primario del transporte global de oxígeno. Se ha medido tradicionalmente usando métodos invasivos cuyos riesgos excedían en ocasiones las ventajas de su monitorización. En este contexto, la minimización del riesgo de la monitorización del gasto cardíaco en nuevas tecnologías no invasivas podría traducirse en mayores ventajas para médicos, hospitales y pacientes: facilidad de uso, disponibilidad del equipamiento y menor tiempo de recuperación y mejores resultados en el paciente. La impedancio-cardiografía o cardiografía de impedancia (ICG} es una prometedora tecnología no invasiva para la monitorización del gasto cardíaco. Sin embargo, la información disponible sobre las señales de ICG es más escasa que otras señales fisiológicas como el electrocardiograma (ECG). La presente Tesis Doctoral contribuye al desarrollo de técnicas de tratamiento de señal de ICG para así crear un monitor hemodinámico innovador. En primer lugar, se proporciona una extensa revisión bibliográfica sobre los aspectos básicos del contexto clínico en el que se utiliza la monitorización del gasto cardíaco así como sobre el estado del arte de los monitores de gasto cardíaco que existen en el mercado. Esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos que también se explican en detalle. Dichos datos clínicos también son útiles para complementar las explicaciones teóricas de los índices de paciente de variabilidad cardíaca y el flujo y la presión sanguíneos. Además, se presenta un nuevo método de creación de señales sintéticas biomédicas con características de tiempo-frecuencia conocidas. Uno de los primeros análisis de esta Tesis Doctoral estudia la diferencia temporal entre los picos de los latidos cardíacos del ECG y del ICG: el segmento RC. Este segmento RC es una medida del retardo temporal entre la actividad eléctrica y mecánica del corazón. Se analiza la relación del segmento RC con la presión arterial y el intervalo cardíaco. La concordancia entre la duración de los latidos del ECG y del ICG es uno de los resultados claves para desarrollar nuevos algoritmos de detección de artefactos y el segmento RC también podría ser relevante en la descripción de la hemodinámica de los pacientes. Las distribuciones de tiempo-frecuencia (TFD, por sus siglas en inglés) se utilizan para caracterizar cómo el contenido de las señales de impedancia cardiográfica cambia con el tiempo. Dado que las TFDs deben calcularse usando núcleos (kernels, en inglés) concretos, se presenta un nuevo método para seleccionar el mejor núcleo mediante el uso de señales sintéticas. Las TFDs de ICG optimizadas se calculan para extraer distintas características que son usadas para discriminar entre los diferentes estados de anestesia en pacientes sometidos a procesos quirúrgicos. Las características derivadas de las distribuciones de tiempo-frecuencia también son utilizadas para describir las operaciones quirúrgicas durante toda su extensión temporal. La relación entre dichas características son analizadas y se proponen distintos modelos de predicción para el gasto cardíaco. Estos modelos de predicción demuestran que las características derivadas de las distribuciones tiempo-frecuencia de señales de ICG están relacionadas con el gasto cardíaco de los pacientes. Finalmente, se presenta un estudio de validación del monitor qCO, diseñado con alguna de las técnicas que son consecuencia de esta Tesis Doctoral. Las principales conclusiones de este trabajo han sido protegidas con una patente que ya ha sido registrada. Como conclusión, esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos y una variedad de técnicas de procesado y análisis de señales de cardiografía de impedancia que han sido incluidas en dispositivos biomédicos disponibles en el mercad