From the need to the knowledge. Feeding emotions and thoughts: Assessing emotion regulation strategies in food tribes

Some people choose to follow a certain dietary habit because they are guided by strong beliefs, but also to pursue a healthy lifestyle. Food tribes are defined as social groups sharing common values and beliefs that underlie food and lifestyle choices. The current study aimed to investigate potential differences in emotion regulation between specific food tribes, and when comparing food tribes to people who conversely do not follow a dietary habit. Specifically, we examined the presence of rumination, psychological inflexibility, and emotional contagion as components of emotion dysregulation. A Google form was developed to obtain all the information needed to respond to our research questions and goals, and investigate the following food groups: Veganism, Fruitarianism, Raw Food Diet, Paleodiet, Blood Type group, Breatharianism, Macrobiotic Diet, and Other. The final sample consisted of 378 healthy subjects (117 males, 258 females, 3 other) ranging in age from 18 to 70 years. The first part of Google form consisted of general questions about the participant's dietary habits while the second part included specific questionnaires assessing the psychological constructs we investigated. Vegan people were less conditioned by others in their food choices when compared to the other food tribes. The Paleodiet group associated its food choice with health reasons as well as the Blood Type group. Differences were found in the presence of cognitive inflexibility and rumination, reflective thinking, emotional dysregulation, emotional contagion and also in the representation of their real and ideal (food) communities. Clinical implications are discussed.

Probabilistic assessment of Autonomic Nervous System fluctuations during tilt table tests

A number of reports have advocated the use of Heart Rate Variability (HRV) as a non invasive method of monitoring the Autonomic Nervous System (ANS). In the anesthesia and critical care monitoring settings, the development of an instrument able to provide real-time information about the ANS state at different stages of any procedure would provide improved safety for patients undergoing diagnostic or therapeutic interventions. However, real-time analysis of HRV can be particularly challenging since larger effective lengths of observation provide better spectral resolution. Our study explores a probabilistic approach that analyzes changes in HRV parameters obtained from an autoregressive (AR) model technique using Burg's methods to evaluate very short observation windows while preserving appropriate frequency resolution. These HRV parameters are continuosly compared to a baseline state, and a probability trend is updated during provocative maneuvers. Preliminary results show that trends from classical parameters such as RMSSD and LFn are consistent and reliable instruments capable of providing significant information about ANS fluctuations in a timely fashion.

Effect of autonomic nervous system dysfunction on sudden death in ischemic patients with anginal syndrome died during electrocardiographic monitoring in Intensive Care Unit

AIM

The aim of this study was to investigate the role of sympathovagal imbalance in patients with ''ischemic'' sudden death (arrhythmic death preceded by ST segment shift). Although heart rate variability is a powerful tool for risk stratification after myocardial infarction, the mechanism precipitating sudden death is poorly known.

METHODS

We analyzed the records of 10 patients who had ischemic sudden death during ECG Holter monitoring. Thirty patients with angina and transient myocardial ischemia during Holter monitoring served as control subjects. Arrhythmias, ST segment changes and heart rate variability were analyzed by a computed interactive Holter system.

RESULTS

In 8 patients the sudden death was induced by ventricular fibrillation; in 2 by atrioventricular block followed by sinus arrest. All 10 patients showed ST segment shift. ST depression (maximal change 0.54+/-0.16 mV) occurred in 6 patients and ST elevation (maximal change 0.65+/-0.24 mV) in 4. The standard deviation of normal RR intervals (SDNN) was 92+/-30 ms during total Holter monitoring period vs 70+/-10 ms and 46+/-8 ms in epoch 1 and epoch 2 respectively. The SDNN was lower before the occurrence of ischemic sudden death: 54+/-12 ms (P< 0.005) in epoch 3 and 26+/-5 (P<0.005) in epoch 4 (i.e. 5 min before the onset of fatal ST segment shift). In controls the SDNN was 108+/-30 ms during total Holter monitoring period, whereas is measured 58+/-28 ms 5 min before the most significant episode of ST shift vs 26+/-5 in the group with sudden death (P<0.001).

CONCLUSION

Sympathovagal imbalance, as detected by a marked decrease in heart rate variability, is present in the period (5 min) immediately preceding the onset of the ST shift precipitating ischemic sudden death. These findings suggest that transient autonomic dysfunction may facilitate, during acute myocardial ischemia, fatal arrhythmias precipitating in sudden death.

Design of a methodology for assessing an electrocardiographic telemonitoring system

Recent studies in Bioengineering show a great interest in telemedicine projects, it is motivated mainly for the fast communication technologies reached during the last decade. Since then many telemedicine projects in different areas have been pursued, among them the electrocardiographic monitoring, as well as methodological reports for the evaluation of these projects. In this work a methodology to evaluate an electrocardiographic telemonitoring system is presented. A procedure to verify the operation of Data Acquisition Module (DAM) of an electrocardiographic telemonitoring system is given, taking as reference defined standards, and procedures for the measurement of the Quality of Service (QoS) parameters required by the system in a Local Area Network (LAN). Finally a graphical model and protocols of evaluation are proposed.

Stress ECG and laboratory database for the assessment of diabetic cardiovascular autonomic neuropathy

Development of a diabetic patient database in order to study Cardiovascular Autonomic Neuropathy (CAN) using as a primary source, stress ECG is presented. The selected platform (ecgML) allows user-friendly environment to analyze and interpret graphs, signals and data. It also allows the ability to perform annotations and reports done by users from different fields. In order to feed the database, the input data is codify using MatLab. The database is composed by two populations: 1) Type 2 Diabetes mellitus group and 2) a control group with no medical history of cardiovascular disease. At the present, there are 62 records available from these two groups. The database also contains laboratory parameters, concurrent medical diagnoses reports verified by cardiologists and other clinicians, automatic annotations for each beat and trend series from parameters extracted from the ECG signals such as RR intervals and ST segment measurements. All this information will become very useful for CAN investigations.

LF/(LF+HF) index in ventricular repolarization variability correlated and uncorrelated with heart rate variability

The purpose of this study, was to asses whether LF/(LF+HF) obtained from ventricular repolarization variability (VRV) reflects the state of sympathovagal balance. The VRV time series and heart rate variability (HRV) time series from seventy two electrocardiogram (ECG) records in four different autonomic nervous system (ANS) profiles (athletes, cardiac transplant patient, heart failure patients and normal subjects) were extracted. A dynamic linear parametric model was applied to separate the VRV in two parts, VRV correlated with HRV (VRV(r)) and VRV uncorrelated with HRV (VRV(u)). Spectral indices were obtained from HRV, VRV, VRV(u) and VRV(u) time series. Changes of these indicators from rest to tilt position were analyzed. Results showed that: i) only LF/(LF+HF) from HRV time series increases significantly from rest to tilt in all ANS profiles, this information could not be retrieved in the other three series (VRV, VRV (u) and VRV(u)) ii) LF/(LF+HF) index in HRV series are significantly different between normal subjects and heart failure patients, while cardiac transplant patients show a low coherence between HRV and VRV power spectra and iii) HF rhythm in VRV series seem to be related to the mechanical effect of respiration.

Real-time ECG transmission via Internet for nonclinical applications

Telemedicine is producing a great impact in the monitoring of patients located in remote nonclinical environments such as homes, elder communities, gymnasiums, schools, remote military bases, ships, and the like. A number of applications, ranging from data collection, to chronic patient surveillance, and even to the control of therapeutic procedures, are being implemented in many parts of the world. As part of this growing trend, this paper discusses the problems in electrocardiogram (ECG) real-time data acquisition, transmission, and visualization over the Internet. ECG signals are transmitted in real time from a patient in a remote nonclinical environment to the specialist in a hospital or clinic using the current capabilities and availability of the Internet. A prototype system is composed of a portable data acquisition and preprocessing module connected to the computer in the remote site via its RS-232 port, a Java-based client-server platform, and software modules to handle communication protocols between data acquisition module and the patient's personal computer, and to handle client-server communication. The purpose of the system is the provision of extended monitoring for patients under drug therapy after infarction, data collection in some particular cases, remote consultation, and low-cost ECG monitoring for the elderly.

Atrial activity enhancement by Wiener filtering using an artificial neural network

This paper describes a novel technique for the cancellation of the ventricular activity for applications such as P-wave or atrial fibrillation detection. The procedure was thoroughly tested and compared with a previously published method, using quantitative measures of performance. The novel approach estimates, by means of a dynamic time delay neural network (TDNN), a time-varying, nonlinear transfer function between two ECG leads. Best results were obtained using an Elman TDNN with nine input samples and 20 neurons, employing a sigmoidal tangencial activation in the hidden layer and one linear neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT-BIH arrhythmia database and compared with an adaptive cancellation scheme proposed in the literature. Results show the advantages of the proposed approach, and its robustness during noisy episodes and QRS morphology variations.

[High resolution ECG signals in Chagas patients: SEARCH project]

This work presents contributions to the study of a public health problem known as Chagasic Myocarditis. The results of the efforts done in Venezuela to understand the evolution of this disease through a novel technique called High Resolution Electrocardiography (HRECG) are discussed. A review of this methodology is presented and also its potential as a tool to study Chagas disease and to diagnose its different myocardial manifestations is discussed in detail. Several research approaches are presented as well as the results obtained, new techniques for HRECG interpretation such as the analysis of signals from the P-R segment, the intra QRS potentials, and late potentials. This method contributes to early detection and follow up of Chagas myocarditis. The collection and the organization of a HRECG data base that served as the basis for SEARCH, which is a valuable resource for new research lines is also described.

Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring

Information management for critical care monitoring is still a very difficult task. Medical staff is often overwhelmed by the amount of data provided by the increased number of specific monitoring devices and instrumentation, and the lack of an effective automated system. Specifically, a basic task such as arrhythmia detection still produce an important amount of undesirable alarms, due in part to the mechanistic approach of current monitoring systems. In this work, multisensor and multisource data fusion schemes to improve atrial and ventricular activity detection in critical care environments are presented. Applications of these schemes are quantitatively evaluated and compared with current methods, showing the potential advantages of data fusion techniques for event detection in noise corrupted signals.

An approach to intelligent ischaemia monitoring

The paper describes an approach to intelligent ischaemia event detection based on ECG ST-T segment analysis. ST-T trends are processed by means of a Bayesian forecasting approach using the multistate Kalman filter. A complete procedure, intended for use in CCU/ICU monitoring areas, is proposed, in order to give the clinician an intelligent monitoring tool. The approach serves to describe trends and their changes in a symbolic way. A novel aspect is its ability to observe certain features of ST-T elevation/depression not detected by other means, and to reject artefacts and erroneous events. A sensitivity of 89.58% and a predictivity of 84.31% are obtained on selected records of the European ST-T database. Using a restriction on event amplitude, the predictivity is raised to 95.55%. An ischaemia sensitivity index of 1.2 was determined. The method has been shown to be a robust and practical trend analysis tool, and seems to be appropriate for numeric/symbolic transformations in next-generation intelligent monitoring systems.

Knowledge-based approach to the management of serious arrhythmia in the CCU

An expert system (SETA) for the management of patients in the CCU environment has been developed. SETA suggests therapeutic actions for the treatment of serious arrhythmias that complicate the pathophysiological state of patients recovering from acute and suspected myocardial infarction. The prototype begins by reasoning from the arrhythmia, diagnosed from the ECG signal, and progresses through its inference process by considering ECG changes (i.e. heart rate, QRS width), patient clinical data, patient history and therapeutic drug data, to reach the most appropriate actions for each particular patient. The system was implemented on production rules using M1 as a development tool. SETA uses a multiknowledge base (KB) architecture, one for each particular arrhythmia and relevant complication, and a decision board that controls the firing of the KBs and keeps track of patient status through time. The system takes into consideration aspects that are very important for the human expert, e.g. sequence of arrhythmia appearance, drug contraindications and priority in the case of simultaneous arrhythmia. The development of this system has given insight into the management of critical CCU patients, that should influence the specification and design of intelligent instruments for this clinical environment.

Microcomputer-based coronary care unit central station

A four-bed central station that can be connected to any commercial intensive-care bedside monitor was developed. The system is based on a personal computer (IBM-AT compatible) as a local unit and on a microcontroller Intel 8031 as a remote unit. Four ECG signals are low-pass filtered, multiplexed, sampled at 256-Hz per channel, 8-bit A/D converted, preprocessed, and converted to a serial format RS-232 by the remote unit. The real-time display of the signals is at the standard speed of 25 and 50 mm/sec. Heartrate, alarms, trend plots, and general patient data are shown on an Olivetti M280 and EGA 13'' color monitor as the local unit. The communication speed was set at 57.6 Kbaud full duplex. Additionally, to reach standard monitoring sweep rates using a 13'' screen with 640 x 350 pixels, an ECG data-compression algorithm was implemented in the remote unit. This unit can support up to eight input channels and can work with any personal computer, via RS-232, with the appropriate software. It also allows other signal preprocessing software that could be developed, such as QRS detection or ST segment quantification, to be loaded into its random access memory and to be run under PC command. The development of this system demonstrated the use of a widespread piece of commercial equipment, the PC, in a very specific application, CCU monitoring, assuring low-cost system implementation. This feature is particularly attractive in upgrading existing CCU units in less developed countries.