Long-term outcome after heart transplantation predicted by quantitative myocardial blush grade in coronary angiography.

The purpose of our study was to investigate whether the quantification of myocardial blush grade (MBG) during surveillance coronary angiography can predict long-term outcome after heart transplantation (HT). In 105 HT recipients who underwent cardiac catheterization, cardiac allograft vasculopathy (CAV) was assessed visually using the ISHLT grading scale (prospective cohort study). MBG was quantified by dividing the plateau of contrast agent gray-level intensity (G(max)) by the time-to-peak intensity (T(max)). In a subgroup (n = 72), myocardial perfusion index by cardiac magnetic resonance imaging (CMR) was assessed. During a mean follow-up duration of 2.7 (standard deviation [SD] 1.0) years, 26 patients experienced cardiac events, including 7 with cardiac death and 19 who underwent coronary revascularization. G(max)/T(max) was related to CAV by ISHLT criteria and to subsequent cardiac events. By univariate analysis, patient age, organ age, CAV, MBG and myocardial perfusion index by CMR were all predictive for cardiac events. Multivariable analysis demonstrated that G(max)/T(max) provided the most robust prediction of cardiac death (hazard ratio [HR] = 0.2, 95% confidence interval [CI] = 0.06-0.64, p < 0.01) and cardiac events (HR = 0.52, 95% CI = 0.32-0.84, p < 0.01), beyond clinical parameters and the presence of CAV. G(max)/T(max) is a valuable surrogate parameter of microvascular integrity, which is associated with cardiac death and revascularization procedures after HT.

[Biomedical engineering supports surgical planning and interventions]. / OP-Planung und OP-Unterstützung durch biomedizinische Technik.

Fundamental changes were achieved with the introduction of minimally invasive surgery. In this context, innovations in microtechnology played a significant role in the deployment of new tools. Developments for further integration are still ongoing. Furthermore, decisive progress was made by the timely provision of individual patient data prior to surgery. These comprise imaging data, electrophysiological or functional recordings, and synthetic data gained by modeling and simulation of anatomical or physiological conditions. Aside from the technical aspects of supporting surgery, effective quality management and optimized workflow are essential for therapeutic success. The vision of autonomously operating robots has been dropped in favor of permanently conducted and supervised interventions with the support of intelligent tools for the surgeon. Recent advances in reconstruction and transplantation surgery by tissue engineering and molecular biology are only the beginning of new promising concepts.

Challenges in the evolution of the medical informatics program at heidelberg/heilbronn (Germany).

OBJECTIVE: Health and medical informatics (HMI) is an evolving discipline. Therefore, evolving educational programs in HMI have to take a variety of requirements into account. The aim of this paper is to analyze these requirements and to compare them with the medical informatics program Heidelberg/Heilbronn, Germany. METHODS: Systematic analysis of the IMIA recommendations on educating HMI, the Bologna declaration, current technological and health care developments and the results of graduates surveys. RESULTS: The latest revision of the Heidelberg/Heilbronn medical informatics program not only takes current developments into account but also realizes the IMIA recommendations, the Bologna declaration and graduates' data and feedback obtained in structured surveys. The topics bioinformatics, IT security and telemedicine were strengthened, taking major research and application trends into account. The program has been transformed into a consecutive bachelor/master program. It qualifies its graduates to work in the field of medical informatics as well as in informatics. CONCLUSIONS: Medical informatics is a very broad field. Programs have to make concessions to scope: It is not possible to provide profound knowledge and skills in computer science and also teach a variety of application areas like bioinformatics, public health informatics and clinical informatics in depth within one medical informatics program. Many graduate programs in various nations concentrate on providing HMI skills to health care professionals.

Detection of sleep apnea episodes from multi-lead ECGs considering different physiological influences.

OBJECTIVES: This article deals with recognition of sleep apnea, using solely information available from multi-lead ECGs. METHODS: Characteristic variations in heart rhythm and amplitude of the ECG are compared with respect to their diagnostic accuracy by means of an ROC analysis that is performed on a local similarity index. In 38 8-lead ECGs, each minute is classified with respect to occurrence of apnea events and the result is validated against expert annotations derived from synchronized polysomnographic recordings. Moreover, the results are compared to those obtained from the well known Physionet apnea-ECG database. RESULTS: Whereas the effect of amplitude modulation yields consistent results on both data sets (ROC-area 89.0% vs. 88.3%), a remarkable loss in performance is observed for the frequently applied heart rhythm (89.8% vs. 77.9%). Examples illustrating the reasons for this difference are given and discussed. With respect to aggregation of multi-lead information, two methods (PCA vs. averaging) are compared. The results indicate that averaging performs better (89.3%) than the adaptively estimated PCA (87.2) even when applied to a reduced set of leads. CONCLUSIONS: It is concluded that sleep apnea recognition from heart rhythm should always be complemented by analysis of the amplitude variations of the ECG.

Tonic descending inhibition affects intensity coding of nociceptive responses of spinal dorsal horn neurones in the cat.

The supraspinal inhibitory control of lumbar spinal dorsal horn neurones was investigated in N2O-anaesthetized cats by reversibly blocking conduction in the spinal cord. Dorsal horn neurones selected for this study had convergent input from myelinated (A-) and unmyelinated (C-) fibres in the posterior tibial and/or superficial peroneal nerves of the hind limb. Virtually all of them could also be excited by noxious heating of the skin of the footpad region and by low intensity mechanical stimulation of the foot. Variation of the temperature of noxious radiant skin heating (40-56 degrees C, 10 sec in duration) resulted in graded responses of the neurones. The stimulus-response functions (SRF) were monotonic; in the majority of 32 cases they were linear. Neurones could be classified according to their maximum discharge frequency in response to skin heating into 22 weakly sensitive units (responses below 100 Hz at 50 degrees C) and 10 highly sensitive units (above 100 Hz). Responses outlasted the period of skin heating by seconds to minutes. A reversible conduction block of spinal axons by cooling a 15 mm cord segment (L1) with a thermode at 0 degrees C affected the responsiveness of the dorsal horn neurones in 12 of 15 cases. The maximum discharge frequency to a certain temperature of skin heating was increased during the spinal block. The duration of heat-evoked discharges was either not changed or increased during the spinal block. The SRF were reversibly displaced during the spinal blockade to higher discharge frequencies and lower threshold temperatures of skin heating. In 8 of 12 cases the change in the SRF was a nearly parallel shift, whereas in 4 units the increase of responsiveness had a complex effect upon the SRF. The decrease in the threshold to skin heating ranged up to 4.5 degrees C; the mean decrease was 2 degrees C. It is confirmed that in anaesthetized cats, nociceptive spinal neurones are subject to a tonically active descending inhibition, which is interrupted by local spinal cooling. The effect of the spinal block on the SRF of the neurones suggests that this tonic inhibition is similar to that produced by electrical stimulation in the lateral reticular formation of the brain-stem.

Time-on-task analysis using wavelet networks in an event-related potential study on attention-deficit hyperactivity disorder.

OBJECTIVE: The aim of this event-related potential (ERP) study was to test time-on-task analysis at the level of single sweeps in a clinical trial. Since inattentiveness is one of the main symptoms of attention-deficit hyperactivity disorder (ADHD), this child psychiatric disorder was chosen as an exemplary application. METHODS: Twenty-four healthy and 24 ADHD boys, aged 9--15 years, performed an auditory selective attention task for about 5 min. ERP single trials were analyzed using wavelet networks. Time-on-task analysis was applied to omission errors, reaction time and slow ERP components (frontal negativity, parietal positivity), represented by a low-frequency wavelet component. RESULTS: Both performance and ERP measures showed distinct temporal dynamics. Time-on-task effects were not only linear, but also of higher order and started after less than 1 min. For ADHD children, earlier time-on-task effects, i.e. an earlier increase of omission errors and frontal negativity, resulted. Healthy children could allocate more attentional resources during the course of the experiment. CONCLUSION: Time-on-task analysis at the level of single trials revealed phenomena probably reflecting ADHD children's attentional deficits. Thus, a more differentiated ERP analysis may provide a better understanding of the pathophysiological background in neuropsychiatric disorders.

Single-sweep analysis of event-related potentials by wavelet networks--methodological basis and clinical application.

OBJECTIVE: Trial-to-trial variabilities in event-related potentials (ERP's), which are neglected by investigating averaged ERP's, can be important to establish group-specific effects in clinical studies. Single ERP responses have to be analyzed to quantify these variations. In order to overcome the disadvantages of existing single-sweep estimators, we have developed a new procedure based on wavelet networks (WN's) and applied this novel approach in a study concerning attention deficit hyperactivity disorder (ADHD) in children. METHOD: WN's represent signals as a linear combination of wavelet nodes, i.e., components characterized by time-frequency features related to the wavelet transformation. In single-sweep analysis, each wavelet node is restricted to a specific region of the time-frequency plane during the recursive WN training process. This is achieved by means of tapering and bandpass filtering with Gaussian functions which are automatically adapted and closely related to the Morlet basis wavelet. The time course of a single event-related response can be reliably estimated. Furthermore, the WN method automatically provides well-defined parameters for single event-related responses, respectively ERP trial-to-trial variabilities. RESULTS: In a psychophysiological study on ADHD using auditory evoked potentials (AEP's), latency and amplitude parameters extracted from averaged ERP's did not reveal any significant differences between 25 control and 25 ADHD boys. In contrast, interesting group-specific differences could be established by WN single-sweep analysis. CONCLUSION: WN single-sweep analysis can be recommended as a sensitive tool for clinical ERP studies which should be applied in addition to the investigation of averaged responses. INDEX TERMS: Attention deficit hyperactivity disorder (ADHD), event-related potentials, single-sweep estimation, single-sweep parameterization, time-frequency method, wavelet networks.

A comparative summary of six Health/Medical Informatics programs.

This paper tries to compare six Health Informatics/Medical Informatics (HI/MI) programs by a number of attributes in standardized form. The different programs and their curricula were summarized at the 5th IMIA Working Conference on HI/MI Education at Heidelberg/Heilbronn. The presentation is condensed to a synoptical scheme. Most of the information used for this purpose is taken from five individual papers about the programs presented in this special issue of Methods of Information in Medicine.

Time-frequency analysis of ventricular late potentials.

The time-frequency characteristics are studied of averaged and filtered ECG records from 21 patients with sustained ventricular tachycardia and 29 healthy control subjects. Simulated data as well as real ECG records reveal the detection accuracy of the wavelet transform of signals with late potentials. The wavelet-transforms of preprocessed ECG signals are plotted in the time-frequency plane. These representations of the signals are well suited to describe the different characteristics of the patients and healthy subjects. A quantitative discrimination was performed with a sensitivity of 90% and a specificity of 72% by the energy underneath the squared modulus of the time-frequency distribution plots of the computed wavelet transforms.

[Inhibition in the spinal cord: a neuronal mechanism of hypalgesia produced by acupuncture (author's transl)]. / Hemmung im Rückenmark, ein neurophysiologischer Wirkungsmechanismus bei der Hypalgesie durch Stimulationsakupunktur.

Central nervous system mechanisms of nociception and pain were studied electrophysiologically in the spinal cord of cats. A great proportion of dorsal horn neurons respond to noxious skin stimuli, for instance to heating to 50 degrees C; such stimuli predominantly activate afferent C fibres. These spinal neurons participate in the transmission of nociceptive information to the brain. The heat-evoked discharges of dorsal horn neurons are effectively inhibited by repetitive electrical stimulation of large;, low-threshold cutaneous A fibres. The inhibition reaches its maximum effect after several minutes of nerve stimulation, and declines at a slow rate after the end of stimulation. This long-term suppression is considered to be a neuronal mechanism underlying hypalgesia produced in man e.g. by acupuncture and by transcutaneous nerve stimulation.

Quantification of ECG late potentials by wavelet transformation.

Late potentials of preprocessed ECG recordings from patients with sustained ventricular tachycardia and healthy control subjects are investigated by the wavelet transformation. The advantages of this non-stationary signal processing method are discussed in comparison with FFT spectrograms of artificial test signals. The energy distribution plots of these wavelet transformed test signals demonstrate the superiority in detection accuracy and frequency resolution compared with the corresponding spectrograms. For this reason a quantitative discrimination between the two clinical groups of patients and healthy subjects was performed by calculating the scalograms of the wavelet transformed ECG signals in the time-frequency plane. The energy in the frequency band of 100-300 Hz during the last time segment of the QRS complex yielded best classification results.

Representations of ECG--late potentials in the time frequency plane.

It is shown that time frequency signal analysis could be a useful technique in detecting late potentials (LP). In particular, the spectrogram, the Wigner distribution and the wavelet transform have been applied to ECG signals from patients with sustained ventricular tachycardia. Comparisons of the three algorithms reveals that much better time localization is achieved by the wavelet transform. Moreover, it is concluded that a separation between healthy subjects and patients could be obtained based upon time frequency analysis.

EP parametrization and classification using wavelet networks--theoretical concept and medical application.

The purpose of this paper is to outline the fundamental concept of wavelet networks (WNs) and to demonstrate its specific advantages in a clinical discrimination task. A group of 25 boys with attention deficit hyperactivity disorder (ADHD) should be separated from a control group of 25 healthy boys by auditory evoked potentials. Because of the high variability of the recorded EP signals quantification of the averaged sweeps by peak latencies and amplitudes failed. However, with wavelet networks a maximum classification rate of 80% was achieved by crossvalidation. A WN is basically described as a multilayer perceptron which consists of two parts for feature extraction/parametrization and classification. These essential steps of a pattern recognition task are not separated in different tasks but linked together by the clamp of the learning algorithm. Because no user interaction is necessary we call this procedure a self-learning method.

[Computer-assisted system for analysis of heart rate variability in clinical studies]. / Ein Computergestütztes System zur Analyse der Herzfrequenzvariabilität im Rahmen klinischer Studien.

This paper describes an adaptable framework that facilitates exploratory analysis, interpretation and classification of beat-to-beat data extracted from the electrocardiogram (ECG). The system supports a variety of user-defined annotations and allows the definition of analysis programs. Special care is taken on the correct treatment of corrupted and missing data, ubiquitously found in real world problems. Besides the computation, the performance of single features can be inspected using different kinds of diagrams provided by the system. Combinations of features can be evaluated using a polynomial classifier. Both the computation and combination of features are defined as tasks that can be dispatched by a server to various clients. The framework is easily adaptable to different problem structures and has been used successfully in three studies.

[A computer system for planning and carrying out neurosurgical interventions]. / Ein Computersystem zur Planung und Durchführung Neurochirurgischer Interventionen.

Neurosurgical interventions have to be planned carefully using different sources of information like anatomical MR images, segmented brain structures, functional data (EEG, MEG, fMRI) and atlas information. We developed a 3D planning system that incorporates this important data. The planning procedure is performed by the neurosurgeon in less than 15 minutes. The results of the planning phase, i.e. an optimal trajectory, localizations of the electrical sources and information about the brain tissue can be used intraoperatively. Therefore the planning system is connected with a navigation system. The simultaneous visualization of the planning information and the actual position of the instrument during the surgical procedure is extremely valuable for the outcome and quality of the intervention.

Robust detection of sleep apnea from Holter ECGs. Joint assessment of modulations in QRS amplitude and respiratory myogram interference.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". OBJECTIVES: Detect presence of sleep-related breathing disorders (SRBD) in epochs of 1 min by signal analysis of Holter ECG recordings. METHODS: In 121 patients, 140 synchronized polysomnograms (PSGs) and 8-channel Holter ECGs were recorded. The only excluded condition was persistent arrhythmias. Respiratory events as scored from the PSGs were mapped to a 1 min grid and served as reference for ECG-based detection. Moreover, 69/70 recordings of the Physionet Sleep Apnea ECG Database (PADB) were included. We performed receiver operating characteristics analysis of a single, novel time-domain feature, the joint local similarity index (jLSI). Based on cross-correlation, the jLSI quantifies the time-locked occurrence of characteristic low-frequency (LF) modulations in ECG respiratory myogram interference (RMI), QRS amplitude (QRSA) and heart rate. RESULTS: Joint oscillations in QRSA, RMI and the envelope of RMI identified positive epochs with a sensitivity of 0.855 (PADB: 0.873) and a specificity of 0.86 (PADB: 0.88). Inclusion of heart rate did not improve detection accuracy. CONCLUSIONS: Joint occurrence of LF-modulations in QRSA and RMI is a characteristic feature of SRBD that is robustly quantified by the jLSI and permits reliable and reproducible detection of sleep apnea in very heterogeneous settings.

Biomedical informatics--a confluence of disciplines?

BACKGROUND: Biomedical informatics is a broad discipline that borrows many methods and techniques from other disciplines. OBJECTIVE: To reflect a) on the character of biomedical informatics and to determine whether it is multi-disciplinary or inter-disciplinary; b) on the question whether biomedical informatics is more than the sum of its supporting disciplines and c) on the position of biomedical informatics with respect to related disciplines. METHOD: Inviting an international group of experts in biomedical informatics and related disciplines on the occasion of the 50th anniversary of Methods of Information in Medicine to present their viewpoints. RESULTS AND CONCLUSIONS: This paper contains the reflections of a number of the invited experts on the character of biomedical informatics. Most of the authors agree that biomedical informatics is an interdisciplinary field of study where researchers with different scientific backgrounds alone or in combination carry out research. Biomedical informatics is a very broad scientific field and still expanding, yet comprised of a constructive aspect (designing and building systems). One author expressed that the essence of biomedical informatics, as opposed to related disciplines, lies in the modelling of the biomedical content. Interdisciplinarity also has consequences for education. Maintaining rigid disciplinary structures does not allow for sufficient adaptability to capitalize on important trends nor to leverage the influences these trends may have on biomedical informatics. It is therefore important for students to become aware of research findings in related disciplines. In this respect, it was also noted that the fact that many scientific fields use different languages and that the research findings are stored in separate bibliographic databases makes it possible that potentially connected findings will never be linked, despite the fact that these findings were published. Bridges between the sciences are needed for the success of biomedical informatics.

Central sleep apnea detection from ECG-derived respiratory signals. Application of multivariate recurrence plot analysis.

OBJECTIVES: This study examines the suitability of recurrence plot analysis for the problem of central sleep apnea (CSA) detection and delineation from ECG-derived respiratory (EDR) signals. METHODS: A parameter describing the average length of vertical line structures in recurrence plots is calculated at a time resolution of 1 s as 'instantaneous trapping time'. Threshold comparison of this parameter is used to detect ongoing CSA. In data from 26 patients (duration 208 h) we assessed sensitivity for detection of CSA and mixed apnea (MSA) events by comparing the results obtained from 8-channel Holter ECGs to the annotations (860 CSA, 480 MSA) of simultaneously registered polysomnograms. RESULTS: Multivariate combination of the EDR from different ECG leads improved the detection accuracy significantly. When all eight leads were considered, an average instantaneous vertical line length above 5 correctly identified 1126 of the 1340 events (sensitivity 84%) with a total number of 1881 positive detections. CONCLUSIONS: We conclude that recurrence plot analysis is a promising tool for detection and delineation of CSA epochs from EDR signals with high time resolution. Moreover, the approach is likewise applicable to directly measured respiratory signals.

Classification of sleep stages using multi-wavelet time frequency entropy and LDA.

BACKGROUND: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. OBJECTIVES: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. METHODS: The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. RESULTS: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. CONCLUSIONS: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.