Intraaortic Balloon Pump Counterpulsation and Cerebral Autoregulation: an observational study

The use of Intra-aortic counterpulsation is a well established supportive therapy for patients in cardiac failure or after cardiac surgery. Blood pressure variations induced by counterpulsation are transmitted to the cerebral arteries, challenging cerebral autoregulatory mechanisms in order to maintain a stable cerebral blood flow. This study aims to assess the effects on cerebral autoregulation and variability of cerebral blood flow due to intra-aortic balloon pump and inflation ratio weaning

Physiological time-series investigations of cardiovascular regulation in healthy young adults during physical exercise.

Physiological parameters may be recorded non-invasively to gain information on cardiovascular function which can then characterise populations with various pathologies. Physical exercise produces specific autonomic nervous system (ANS) changes. There has been no comprehensive profiling of cardiovascular function during exercise or simultaneous characterisation of the influence of exercise on cardiac ventricular function and electrical activity. This work aims to address that, using a combination of physiological parameters. Between-lead agreement for ambulatory electrocardiographic (EGG) depolarisation-repolarisation (QT) interval was quantified during rest and exercise. In contrast to cardiac interval (RR) data, between-lead bias and limits of agreement for QT interval data should be quantified when reporting results from an ambulatory EGG system and between-gender QT differences should also be accounted for. EGG electrode location appears to significantly affect QT-RR hysteresis, the shortening of the post-exercise QT interval relative to that at similar heart rates during exercise or pre-exercise rest, further emphasising the need for standardisation of EGG electrode placement. Sample entropy (SampEn) measures data complexity. Few studies have compared SampEn of RR data (SampEn-RR) during exercise, whilst none have examined SampEn for the corresponding QT interval (SampEn-QT). Fractal analysis assesses data correlation and scaling structures. Detrended fluctuation analysis (DFA) provides a scaling exponent (a) which describes these properties. This has not been quantified for RR interval data during post-exercise recovery and has not been reported for QT interval data. Differences in a magnitudes for RR and QT data suggest that these quantities have different fractal properties. Exercise perturbs the resting QT-RR relationship via hysteresis. The QT variability index (QTVI) quantifies the relative autonomic influence on the atrial and ventricular myocardium during rest and exercise. QTVI is a consistent measure of cardiac ventricular function and as such appears to be a more useful index than other parameters based on RR or QT interval alone

Discriminating noise from chaos in heart rate variability : application to prognosis in heart failure

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 103-109).This thesis examines two challenging problems in chaos analysis: distinguishing deterministic chaos and stochastic (noise-induced) chaos, and applying chaotic heart rate variability (HRV) analysis to the prognosis of mortality in congestive heart failure (CHF). Distinguishing noise from chaos poses a major challenge in nonlinear dynamics theory since the addition of dynamic noise can make a non-chaotic nonlinear system exhibit stochastic chaos, a concept which is not well-defined and is the center of heated debate in chaos theory. A novel method for detecting dynamic noise in chaotic series is proposed in Part I of this thesis. In Part II, we show that linear and nonlinear analyses of HRV yield independent predictors of mortality. Specifically, sudden death is best predicted by frequency analysis whereas nonlinear and chaos indices are more selective for progressive pump failure death. These findings suggest a novel noninvasive probe for the clinical management of CHF patients.by Natalia M. Arzeno.M.Eng

Patient Identification with ECG and SaO2 Time Series

Sudden cardiac death is the most common cause of death in United States. Primary prevention implantable cardioverter defibrillators (ICDs) have been the first line to reduce mortality for high-risk patients. Previous work of identifying subjects at greater risk is neither sensitive nor specific. The development of more reliable predictors that could help identify patients that could benefit from these devices is of both academic and public health interest. In this thesis, we study the time series data of both electrocardiogram (ECG) and oxygen saturation (SaO2) signals from patients who received ICD implantation. This study is part of Prospective Observational Study of Implantable Cardioverter Defibrillators (PROSE-ICD). The features for each subject are generated from some statistics of the ECG and SaO2 signals respectively. For ECG signal, the analysis is from both geometry and dynamics perspective. For SaO2 signal, multivariate and dynamics analysis is applied. Our results showed an overall accuracy of 93.2% for patient classification, with no bias towards healthy or HF patients. Further analysis does not show a clear relationship between ECG and SaO2 signals

Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach

INTRODUCTION Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECG’s but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier. METHOD An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms. RESULTS Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naïve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naïve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean κ 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearson’s phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis. CONCLUSIONS Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naïve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment

Herzratenvariabilität bei PatientInnen mit Depression oder der Doppeldiagnose Depression - somatoforme Störungen im Therapieverlauf

Theoretischer Hintergrund: Die Herzratenvariabilität (HRV) gibt Aufschluss über die sympathische und parasympathische Aktivität des autonomen Nervensystems. Eine niedrige HRV stellt ein Mortalitäts- und Morbiditätsrisiko dar und wird auch mit Depression und verschiedenen funktionelle Syndromen in Verbindung gebracht, wobei die Ergebnisse zur HRV bei Depression heterogen sind und eine mögliche komorbide somatoforme Störung oft unberücksichtigt bleibt. Die HRV gilt als Parameter für Gesundheit und Anpassungsfähigkeit und dürfte sich zur Therapieevaluation eignen. Fragestellung: Ziel war es, die HRV bei PatientInnen mit Depression sowie der Doppeldiagnose Depression und somatoforme Störung im Therapieverlauf zu untersuchen und im speziellen auf die Veränderungen durch einen mehrwöchigen stationären Therapieaufenthalt einzugehen. Zudem wurde auch auf die prognostische Bedeutung der HRV für den Therapieerfolg eingegangen werden. Methode: Es nahmen 32 PatientInnen mit depressiver Erkrankung (15 mit Depression, 17 mit Depression und somatoforme Störung) an der Studie teil. Für diese quasiexperimentelle Studie fanden zu Beginn und zu Ende eines etwa neunwöchigen stationären Aufenthalts Messungen statt. Für die 24-Stunden-HRV-Messung wurde der Medilog® AR12plus Digitaler Holter Rekorder eingesetzt. Es wurden neben den Zeit- und Frequenzbereichsparametern SDNN, pNN50, HF-HRV, LF-HRV und VLF-HRV auch die SD1 und SD2 aus dem Poincarégraph sowie die nichtlinearen Parameter DFA α1 und α2 und die Sample Entropie ausgewertet. Als psychologisch-diagnostische Verfahren wurden die Symptomcheckliste-90-Revidiert (SCL-90-R) sowie der Fragebogen zum Gesundheitszustand SF-36 verwendet. Ergebnisse: Die HRV von PatientInnen mit und ohne somatoformer Störung unterschied sich weder in Variabilität noch Komplexität. PatientInnen die Antidepressiva einnahmen hatten eine deutlich reduzierte HRV, wobei diese Unterschiede nicht auf unterschiedliche Schweregrade der Depression zurückgeführt werden konnten. In der Gesamtstichprobe kam es im Therapieverlauf zu einer signifikanten Steigerung der nächtlichen Gesamt- und langfristigen Variabilität wie auch der VLF-HRV. PatientInnen die Antidepressiva einnahmen, zeigten zudem in der Gesamtmessung eine Steigerung des Vagotonus. PatientInnen mit höheren HRV-Werten zu Therapiebeginn konnten besser vom stationären Aufenthalt profitieren. Die SDNN eignete sich zudem zur Vorhersage einer Verbesserung der grundsätzlichen psychischen Belastung und der Depressivität sowie der Werte für Somatisierung, körperlicher Gesundheit und der Beeinträchtigung durch körperliche Schmerzen.Theoretical background: Heart rate variability (HRV) provides information about the sympathetic and parasympathetic activity of the autonomic nervous system. A low HRV poses a mortality and morbidity risk. It is associated with depression and various functional disorders. However, results for HRV in depression are heterogeneous and a possible comorbid somatoform disorder is often disregarded. Moreover, HRV is considered to be an indicator for health and flexibility and should be suitable for the evaluation of therapy. Objectives: The aim was to analyze the HRV of inpatients with depression or with the double diagnosis of depression and somatoform disorder in the course of treatment. A focus lay on examining changes due to a nine week treatment phase. Additionally, the prognostic value of HRV for therapy outcome was evaluated. Method: 32 patients with depressive disorders (15 with depression, 17 with depression and somatoform disorder) were included in the study. In this quasi-experimental study, assessment took place at the beginning and the end of a nine week clinical treatment. To measure the HRV over the course of 24 hours a Medilog® AR12plus digital holter recorder was used. Parameters analyzed were the time and frequency domain measures SDNN, pNN50, HF-HRV, LF-HRV and VLF-HRV, the SD1 und SD2 from the Poincaré plot as well as the nonlinear measures DFA α1 and α2 and the Sample Entropy. The Symptom Checklist-90-Revised (SCL-90-R) and the SF-36 Health Survey were used for psychological assessment. Results: The HRV of patients with and without somatoform disorder differed neither in variability nor in complexity. Patients taking antidepressants had a significantly reduced HRV, which was not attributable to severity of depression. Overall, we observed a significant increase in the nocturnal total variability and the long-term variability as well as the VLF-HRV throughout the course of therapy. Patients taking antidepressants showed an increase in vagal tone. Patients with higher HRV at the beginning of the treatment benefitted more from treatment. SDNN was found to predict improvements with regard to overall psychological distress and the depression as well as somatization, physical health, and bodily pain

Novel Low Complexity Biomedical Signal Processing Techniques for Online Applications

Biomedical signal processing has become a very active domain of research nowadays. With the advent of portable monitoring devices, from accelerometer-enabled bracelets and smart-phones to more advanced vital sign tracking body area networks, this field has been receiving unprecedented attention. Indeed, portable health monitoring can help uncover the underlying dynamics of human health in a way that has not been possible before. Several challenges have emerged however, as these devices present key differences in terms of signal acquisition and processing in comparison with conventional methods. Hardware constraints such as processing power and limited battery capacity make most established techniques unsuitable and therefore, the need for low-complexity yet robust signal processing methods has appeared. Another issue that needs to be addressed is the quality of the signals captured by these devices. Unlike in clinical scenarios, in portable health monitoring subjects are constantly performing their daily activities. Moreover, signals maybe captured from unconventional locations and subsequently, be prone to perturbations. In order to obtain reliable measures from these monitoring devices, one needs to acquire dependable signal quality measures, to avoid false alarms. Indeed, hardware limitations and low-quality signals can greatly influence the performance of portable monitoring devices. Nevertheless, most devices offer simultaneous acquisition of multiple physiological parameters, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Through multi-modal signal processing the overall performance can be improved, for instance by deriving parameters such as heart rate estimation from the most reliable and uncontaminated source. This thesis is therefore, dedicated to propose novel low-complexity biomedical processing techniques for real-time/online applications. Throughout this dissertation, several bio-signals such as the ECG, PPG, and electroencephalogram (EEG) are investigated. %There is an emphasis on ECG processing techniques, as most of the bio-signals recorded today reflect information about the heart. The main contribution of this dissertation consists in two signal processing techniques: 1) a novel ECG QRS-complex detection and delineation technique, and 2) a short-term event extraction technique for biomedical signals. The former is based on a processing technique called mathematical morphology (MM), and adaptively uses subject QRS-complex amplitude- and morphological attributes for a robust detection and delineation. This method is generalized to intra-cardiac electrograms for atrial activation detection during atrial fibrillation. The second method, called the Relative-Energy algorithm, uses short- and long-term signal energies to highlight events of interest and discard unwanted activities. Collectively, the results obtained by these methods suggest that while presenting low-computational costs, they can efficiently and robustly extract biomedical events of interest. Using the relative energy algorithm, a continuous non-binary ECG signal quality index is presented. The ECG quality is determined by creating a cleaned-up version of the input ECG and calculating the correlation coefficient between the cleaned-up and the original ECG. The proposed quality index is fast and can be implemented online, making it suitable for portable monitoring scenarios