Bo von Schéele

A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching

Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho‐physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case‐based decision support system to assist clinicians in performing such tasks. Case‐based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty‐nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness‐of‐fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho‐physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system

OBJECTIVE An important procedure in diagnosing stress-related disorders caused by dysfunction in the interaction of the heart with breathing, i.e., respiratory sinus arrhythmia (RSA), is to analyse the breathing first and then the heart rate. Analysing these measurements is a time-consuming task for the diagnosing clinician. A decision-support system in this area would reduce the analysis task of the clinician and enable him/her to give more attention to the patient. We have created a decision-support system which contains a signal classifier and a pattern identifier. The system performs an analysis of the physiological time series concerned which would otherwise be performed manually by the clinician. METHODS The signal-classifier, HR3Modul, classifies heart-rate patterns by analysing both cardio- and pulmonary signals, i.e., physiological time series. HR3Modul uses case-based reasoning (CBR), using a wavelet-based method for retrieving features from the signals. The system searches for familiar shapes in the signals by comparing them with shapes already stored. We have applied a best fit scheme for handling signals of different lengths, as the length of a breath is highly dynamic. We also apply automatic weighting to the features to obtain a more autonomous system. The classified heart signals indicate if a patient may be suffering from a stress-related disorder and the nature of the disorder. These classified signals are thereafter sent to the second subsystem, the pattern-identifier. The pattern-identifier analyses the classified signals and searches for familiar patterns by identifying sequences in the classified signals. The identified sequences give clinicians a more complete analysis of the measurements, providing them with a better basis for diagnosis. RESULTS AND CONCLUSION We have shown that a case-based classifier with a wavelet feature extractor and automatic weighting is a viable option for building a decision-support system for the psychophysiological domain, as it is at par, or even outperforms other retrieval techniques and is less complex.

A multi-module case-based biofeedback system for stress treatment

OBJECTIVE Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer-assisted biofeedback system helping in classification, parameter setting and biofeedback training. METHODS The decision support system (DSS) analysis finger temperature in time series signal where the derivative of temperature in time is calculated to extract the features. The case-based reasoning (CBR) is used in three modules to classify a patient, estimate parameters and biofeedback. In each and every module the CBR approach retrieves most similar cases by comparing a new finger temperature measurement with previously solved measurements. Three different methods are used to calculate similarity between features, they are: modified distance function, similarity matrix and fuzzy similarity. RESULTS AND CONCLUSION We explore how such a DSS can be designed and validated the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In this case study we show that the case based biofeedback system outperforms trainee clinicians based on a case library of cases authorized by an expert.

Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning

Increased exposure to stress may cause health problems. An experienced clinician is able to diagnose a persons stress level based on sensor readings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision support system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a number of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collaboration with experts and measurements from 24 persons used as reference.

Heart Rate Variability During Choral Singing

Contemporary research implies that choral singing is beneficial to health. Singing various kinds of songs with varied emphasis, emotion, and tempo gives rise to diverse physiological responses. Breathing is assumed to be synchronized during choral singing, and breathing has major influence on heart rate variability (HRV). In this study, we compare HRV responses during choral singing with slow breathing exercises. Thirteen amateur singers’ HRV were studied during a rehearsal of 4 songs framed by 2 slow breathing exercises without audience. The heart rate was generally higher and HRV generally lower during singing compared to the slow breathing conditions. During singing, but not during slow breathing, peak HRV frequency showed considerable variation among the participants. This could be due to either a low degree of synchronization of breathing during singing or other factors overruling the effects of breathing on HRV.

Relaxing on a Bed of Nails : An Exploratory Study of the Effects on the Autonomic, Cardiovascular, and Respiratory Systems, and Saliva Cortisol

OBJECTIVES This study investigated subjective and physiologic responses of lying on a bed of nails (BN) called the Shakti-mat and of listening to relaxing instructions and music. The BN has 6210 sharp-edge 5-mm plastic nails about 5 mm apart. DESIGN Thirty-two (32) healthy participants went through four conditions in randomized orders combining BN and relaxing instructions. RESULTS The subjective pain ratings on the BN increased immediately and reached a peak within 30 seconds. The pain then subsided gradually, indicating a habituation effect. Self-rated relaxation increased over time in all conditions. Systolic and diastolic blood pressures were higher, heart rate was slower, and there was more high-frequency power heart rate variability (HRV), and signs of increasing circulation in the back on the BN. The relaxation instruction especially affected breathing and the HRV-indices standard deviations of normal interbeat intervals and low-frequency power, both known to be responsive to slow breathing. There were no differences in saliva cortisol. CONCLUSIONS Healthy participants habituated to the induced pain on the BN and were able to subjectively relax. When on a BN, signs of both sympathetic and parasympathetic nervous system activity were observed. The pain may hypothetically have triggered a parasympathetic response.

Diagnosis and biofeedback system for stress

Today everyday life for many people contain many situations that may trigger stress, or result in an individual living on an increased stress level under long duration. High level of stress over time may cause serious health problems. It is known that respiratory rate in terms of hyperventilation (defined as low pCO2) is an important factor and can be used in the diagnosis of stress-related dysfunctions. It can also be used for biofeedback training but available measurement of respiratory rate and its metabolic consequences are not especially suitable for home and office use. The aim of this project is to develop a portable sensor system that can measure stress level during everyday situations e.g. at home or at work. The sensor explored here is a finger temperature (FT) sensor. FT reflects changes in sympathetic nervous system (SNS) and not hyperventilation, as SNS is an important marker of stress it is highly relevant. Clinical studies show that finger temperature, in general, decreases with stress however this changed pattern shows large individual variations. Consequently, diagnosis of stress from the FT measurements is difficult even for the clinical experts. Therefore, a computer-based stress diagnosis system is valuable. In this paper a case-based reasoning (CBR) stress management system is presented and evaluated. The results of the evaluation show a promising performance.

Heart Rate Variability During Piano Playing : A Case Study of Three Professional Solo Pianists Playing a Self-Selected and a Difficult Prima Vista Piece

Heart Rate Variability During Piano Playing : A Case Study of Three Professional Solo Pianists Playing a Self-Selected and a Difficult Prima Vista Piece