Magnetocardiography at rest predicts cardiac death in patients with acute chest pain.

Introduction: Sudden cardiac arrest is a major cause of morbidity and mortality worldwide and remains a major public health problem for which better non-invasive prediction tools are needed. Primary preventive therapies, such as implantable cardioverter defibrillators, are not personalized and not predictive. Most of these devices do not deliver life-saving therapy during their lifetime. The individual relationship between fatal arrhythmias and cardiac function abnormalities in predicting cardiac death risk has rarely been explored. Methods: We retrospectively analyzed the measurements at rest for 191 patients with acute chest pain (ACP) magnetocardiographically. Our recently introduced analyses are able to detect inhomogeneities of the depolarization and repolarization. Moreover, electrically silent phenomena-intracellular ionic currents as well as vortex currents-can be measured and quantified. All included ACP patients were recruited in 2009 at Yonsei University Hospital and were followed up until 2022. Results: During half of the follow-up period (6.5 years), 11 patients died. Out of all the included nine clinical, eight magnetocardiographical, and nine newly introduced magnetoionographical parameters we tested in this study, three parameters revealed themselves to be outstanding at predicting death: heart rate-corrected QT (QTc) prolongation, depression of repolarization current IKr + IKs, and serum creatinine (all significant in Cox regression, p < 0.05). They clearly predicted cardiac death over the 6.5 years duration (sensitivity 90.9%, specificity 85.6%, negative predictive accuracy 99.4%). Cardiac death risk was more than ninefold higher in patients with low repolarization reserve and QTc prolongation in comparison with the remaining patients with ACP (p < 0.001). The non-parametric Kaplan-Meier statistics estimated significantly lower survival functions from their lifetime data (p < 0.001). Discussion: To the best of our knowledge, these are the first data linking magnetocardiographical and magnetoionographical parameters and subsequent significant fatal events in people, suggesting structural and functional components to clinical life-threatening ventricular arrhythmogenesis. The findings support investigation of new prevention strategies and herald those new non-invasive techniques as complementary risk stratification tools.

Cardiac magnetic field map topology quantified by Kullback-Leibler entropy identifies patients with clinically suspected myocarditis.

Background: Myocarditis is a condition that can have severe adverse outcomes and lead to sudden cardiac death if remaining undetected. This study tested the capability of cardiac magnetic field mapping to detect patients with clinically suspected myocarditis. This could open up the way for rapid, non-invasive, and cost-effective screening of suspected cases before a gold standard assessment via endomyocardial biopsy. Methods: Historical cardiac magnetic field maps (n = 97) and data from a state-of-the-art magnetocardiography device (n = 30) were analyzed using the Kullback-Leibler entropy (KLE) for dimensionality reduction and topological quantification. Linear discriminant analysis was used to discern between patients with ongoing myocarditis and healthy controls. Results: The STT segment of a magnetocardiogram, i.e., the section between the end of the S wave and the end of the T wave, was best suited to discern both groups. Using a 250-ms excerpt from the onset of the STT segment gave a reliable classification between the myocarditis and control group for both historic data (sensitivity: 0.83, specificity: 0.85, accuracy: 0.84) and recent data (sensitivity: 0.69, specificity: 0.88, accuracy: 0.80) using the KLE to quantify the topology of the cardiac magnetic field map. Conclusion: The implementation based on KLE can reliably distinguish between clinically suspected myocarditis patients and healthy controls. We implemented an automatized feature selection based on LDA to replace the observer-dependent manual thresholding in previous studies.

Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening.

Many sleep centres try to perform a reduced portable test in order to decrease the number of overnight polysomnographies that are expensive, time-consuming, and disturbing. With some limitations, heart rate variability (HRV) has been useful in this task. The aim of this investigation was to evaluate if inclusion of symbolic dynamics variables to a logistic regression model integrating clinical and physical variables, can improve the detection of subjects for further polysomnographies. To our knowledge, this is the first contribution that innovates in that strategy. A group of 133 patients has been referred to the sleep center for suspected sleep apnea. Clinical assessment of the patients consisted of a sleep related questionnaire and a physical examination. The clinical variables related to apnea and selected in the statistical model were age (p < 10(-3)), neck circumference (p < 10(-3)), score on a questionnaire scale intended to quantify daytime sleepiness (p < 10(-3)), and intensity of snoring (p < 10(-3)). The validation of this model demonstrated an increase in classification performance when a variable based on non-linear dynamics of HRV (p < 0.01) was used additionally to the other variables. For diagnostic rule based only on clinical and physical variables, the corresponding area under the receiver operating characteristic (ROC) curve was 0.907 (95% confidence interval (CI) = 0.848, 0.967), (sensitivity 87.10% and specificity 80%). For the model including the average of a symbolic dynamic variable, the area under the ROC curve was increased to 0.941 (95% = 0.897, 0.985), (sensitivity 88.71% and specificity 82.86%). In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea. In addition, the processing of the HRV is a well established low cost and robust technique.

Classifying healthy women and preeclamptic patients from cardiovascular data using recurrence and complex network methods.

It is urgently aimed in prenatal medicine to identify pregnancies, which develop life-threatening preeclampsia prior to the manifestation of the disease. Here, we use recurrence-based methods to distinguish such pregnancies already in the second trimester, using the following cardiovascular time series: the variability of heart rate and systolic and diastolic blood pressures. We perform recurrence quantification analysis (RQA), in addition to a novel approach, ε-recurrence networks, applied to a phase space constructed by means of these time series. We examine all possible coupling structures in a phase space constructed with the above-mentioned biosignals. Several measures including recurrence rate, determinism, laminarity, trapping time, and longest diagonal and vertical lines for the recurrence quantification analysis and average path length, mean coreness, global clustering coefficient, assortativity, and scale local transitivity dimension for the network measures are considered as parameters for our analysis. With these quantities, we perform a quadratic discriminant analysis that allows us to classify healthy pregnancies and upcoming preeclamptic patients with a sensitivity of 91.7% and a specificity of 45.8% in the case of RQA and 91.7% and 68% when using ε-recurrence networks, respectively.

Effect of CPAP therapy on daytime cardiovascular regulations in patients with obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a sleep disorder with a high prevalence that causes pathological changes in cardiovascular regulation during the night and also during daytime. We investigated whether the treatment of OSA at night by means of continuous positive airway pressure (CPAP) improves the daytime consequences. Twenty-eight patients with OSA, 18 with arterial hypertension, 10 with normal blood pressure, were investigated at baseline and with three months of CPAP treatment. Ten age and sex matched healthy control subjects were investigated for comparisons. We recorded a resting period with 20min quiet breathing and an exercise stress test during daytime with ECG and blood pressure (Portapres). The bicycle ergometry showed a significant reduction of the diastolic blood pressure at a work load of 50W and 100W (p<0.05 and p<0.01, respectively) and a decrease of the heart rate recovery time after the stress test (p<0.05). These results indicate a reduction of vascular resistance and sympathetic activity during daytime. The coupling analysis of the resting periods by means of symbolic coupling traces approach indicated an effect of the CPAP therapy on the baroreflex reaction in hypertensive patients where influences of the systolic blood pressure on the heart rate changed from pathological patterns to adaptive mechanisms of the normotensive patients (p<0.05).

Cardiac magnetic field mapping quantified by Kullback-Leibler entropy detects patients with coronary artery disease.

Cardiac magnetic field mapping (CMFM) is a noninvasive method to determine cardiac electrical activity. We analysed the utility of CMFM for the detection of patients with coronary artery disease (CAD) without subjecting them to stress. We studied 59 healthy control subjects and 101 patients with CAD without previous myocardial infarction (MI). The heart's magnetic field was recorded over the anterior chest wall using a multichannel magnetic measurement system with axial second-order gradiometers. The evaluation of CMFM was based on comparison of the 'ideal' group mean maps of young healthy subjects and maps of examined individuals. Three measures of similarity were considered: Kullback-Leibler (KL) entropy, normalized residual magnetic field strength and deviations in the magnetic field map orientation. The mean values of these parameters during the depolarization and repolarization were used for further classification with the help of logistic regression. The feature set based on the KL-entropy demonstrated the best classification results (sensitivity/specificity of 85/80%), followed by the residual feature (85/75%) and the magnetic field orientation feature (80/73%) sets. The forward stepwise technique was applied to select the best set of features from the combined feature set. Two parameters were selected, namely the KL-entropy for the repolarization period and the residual parameter for the depolarization period. The classification based on these parameters demonstrated a sensitivity of 88% and a specificity of 88% for the distinction of CAD patients from the control subjects. The area under the receiver operator curve was 94%. Hence, we suggest that CMFM evaluation based on KL-entropy is a promising technique to identify patients with CAD.

Investigation of an automatic sleep stage classification by means of multiscorer hypnogram.

OBJECTIVES: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. METHODS: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring. RESULTS: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. CONCLUSIONS: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm's assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures.

Cardiac magnetic field map topology quantified by Kullback-Leibler entropy identifies patients with hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a common primary inherited cardiac muscle disorder, defined clinically by the presence of unexplained left ventricular hypertrophy. The detection of affected patients remains challenging. Genetic testing is limited because only in 50%-60% of all HCM diagnoses an underlying mutation can be found. Furthermore, the disease has a varied clinical course and outcome, with many patients having little or no discernible cardiovascular symptoms, whereas others develop profound exercise limitation and recurrent arrhythmias or sudden cardiac death. Therefore prospective screening of HCM family members is strongly recommended. According to the current guidelines this includes serial echocardiographic and electrocardiographic examinations. In this study we investigated the capability of cardiac magnetic field mapping (CMFM) to detect patients suffering from HCM. We introduce for the first time a combined diagnostic approach based on map topology quantification using Kullback-Leibler (KL) entropy and regional magnetic field strength parameters. The cardiac magnetic field was recorded over the anterior chest wall using a multichannel-LT-SQUID system. CMFM was calculated based on a regular 36 point grid. We analyzed CMFM in patients with confirmed diagnosis of HCM (HCM, n=33, 43.8+/-13 years, 13 women, 20 men), a control group of healthy subjects (NORMAL, n=57, 39.6+/-8.9 years; 22 women and 35 men), and patients with confirmed cardiac hypertrophy due to arterial hypertension (HYP, n=42, 49.7+/-7.9 years, 15 women and 27 men). A subgroup analysis was performed between HCM patients suffering from the obstructive (HOCM, n=19) and nonobstructive (HNCM, n=14) form of the disease. KL entropy based map topology quantification alone identified HCM patients with a sensitivity of 78.8% and specificity of 86.9% (overall classification rate 84.8%). The combination of the KL parameters with a regional field strength parameter improved the overall classification rate to 87.9% (sensitivity: 84.8%, specificity: 88.9%, area under ROC curve: 0.94). KL measures applied to discriminate between HOCM and HNCM patients showed a correct classification of 78.8%. The combination of one KL and one regional parameter again improved the overall classification rate to 97%. A preliminary prospective analysis in two HCM families showed the feasibility of this diagnostic approach with a correct diagnosis of all 22 screened family members (1 HOCM, 4 HNCM, 17 normal). In conclusion, Cardiac Magnetic Field Mapping including KL entropy based topology quantifications is a suitable tool for HCM screening.