Heart rate variability analysis during central hypovolemia using wavelet transformation

Alteration autonomic control of cardiac function during hemodialysis predict cardiovascular outcomes in end stage renal disease patients

Dialysis-induced hemodynamic instability has been associated with increased risk of cardiovascular (CV) mortality. However, the control mechanisms beneath the dynamic BP changes and cardiac function during hemodialysis and subsequent CV events are not known. We hypothesize that the impaired hemodynamic control can be prognostic indicators for subsequent CV events in end stage renal diseaes (ESRD) patients. To explore the association of hemodynamic parameters and CV events in hemodialysis patients, we enrolled ESRD patients who received chronic hemodialysis without documented atherosclerotic cardiovascular disease and hemodynamic parameters were continuously obtained from the impedance cardiography during hemodialysis. A total of 35 patients were enrolled. 16 patients developed hospitalized CV events. The statistical properties [coefficient of variance (standard deviation / mean value; CoV)] of hourly beat-to-beat dynamics of hemodynamic parameters were calculated. The CoV of stroke volume (SV) and cardiac index (CI) between the 1^st and 2^nd hour of dialysis were significantly increased in patients without CV events compared to those with CV events. Higher CoV of SV_diff and CI_diff were significantly correlated with longer CV event-free survival, and the area under the receiver operating characteristic (ROC) curve showed fair overall discriminative power (0.783 and 0.796, respectively). The responses of hemodynamic control mechanisms can be independent predictive indexes for lower hospitalized CV events, which implies that these patients who have better autonomic control systems may have better CV outcomes.

The effect of traditional Persian music on the cardiac functioning of young Iranian women

In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are not similar. Therefore, in the present study, we have sought to examine the effects of traditional Persian music on the cardiac function in young women. Twenty-two healthy females participated in this study. ECG signals were recorded in two conditions: rest and music. For each of the 21 ECG signals (15 morphological and six wavelet based feature) features were extracted. SVM classifier was used for the classification of ECG signals during and before the music. The results showed that the mean of heart rate, the mean amplitude of R-wave, T-wave, and P-wave decreased in response to music. Time-frequency analysis revealed that the mean of the absolute values of the detail coefficients at higher scales increased during rest. The overall accuracy of 91.6% was achieved using polynomial kernel and RBF kernel. Using linear kernel, the best result (with the accuracy rate of 100%) was attained.

Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks

Objective:

In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women.

Methods:

Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music.

Results:

Collected data were separated into two data sets train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively.

Conclusion:

The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician’s disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

Massive transfusion prediction with inclusion of the pre-hospital Shock Index

BACKGROUND

Detecting occult bleeding can be challenging and may delay resuscitation. The Shock Index (SI) defined as heart rate divided by systolic blood pressure has attracted attention. Prediction models using combinations of pre-hospital SI (phSI) and the trauma centre SI (tcSI) values may be effective in identifying patients requiring massive blood transfusions (MT).

AIM

To explore whether combinations of the phSI and the tcSI augment MT prediction.

METHODS

The scores were retrospectively developed using all major trauma patients that presented to The Alfred Hospital between 2006 and 2012. The first PH and TC observations were used. To avoid exclusion of the 'sickest' patients, the SI was imputed to 2 where SBP was missing, but HR was present. We developed 4 models. (i) 'Dichotomised', defined as positive when both phSI and tcSI were ≥1. (ii) 'Formulaic', defined by logistic regression analysis. (iii) 'Combination', defined pragmatically based on the logistic regression. (iv) 'Trending', defined as: tcSI minus phSI.

RESULTS

There were 6990 major trauma patients and 360 (5.2%) received MT. There were 1371 cases with either phSI or tcSI missing and were thus excluded from the analysis. The 'Dichotomised' had higher positive predictive value than the tcSI with a further 5 per 100 patients identified. The 'Formulaic' model, defined as: log Odds (MT)=2.16×tcSI+0.89×phSI-5.42, and the 'Combination' model, defined as: phSI×0.5+tcSI, performed equally (AUROC 0.83 versus 0.83, χ(2)=0.86, p=0.35). The 'Formulaic' performed marginally, but statistically significantly, more accurate than the tcSI alone (AUROC 0.83 versus 0.82, χ(2)=6.89, p<0.01). An 'Upward Trending' SI was observed in 1758 patients, revealing a 4.6-fold univariate association with MT (OR 4.55; 95%CI 2.64-7.83), and an AUROC of 0.79 (95%CI 0.74-0.83). The 'Downward Trending' SI was protective against MT (OR 0.44; 95%CI 0.34-0.57).

CONCLUSION

The initial pre-hospital SI is associated with MT. However, this relationship did not clinically augment MT decision when combined with the in-hospital SI. The simplicity of the SI makes it a favourable option to explore further. Computer-assisted technology in data capturing, analysis and prognostication presents avenues for further research.

Challenges and possibilities in forward resuscitation

The environmental and logistical constraints of the prehospital setting make it a challenging place for the treatment of trauma patients. This is perhaps more pronounced in the management of battlefield casualties before extraction to definitive care. In seeking solutions, interest has been renewed in implementing damage control resuscitation principles in the prehospital setting, a concept termed remote damage control resuscitation. These developments, while improving conflict survival rates, are not exclusive to the military environment, with similar situations existing in the civilian setting. By understanding the pathophysiology of shock, particularly the need for oxygen debt repayment, improvements in the assessment and management of trauma patients can be made. Technology gaps have previously hampered our ability to accurately monitor the prehospital trauma patient in real time. However, this is changing, with devices such as tissue hemoglobin oxygen saturation monitors and point-of-care lactate analysis currently being refined. Other monitoring modalities including newer signal analysis and artificial intelligence techniques are also in development. Advances in hemostatic resuscitation are being made as our understanding and ability to effectively monitor patients improve. The reevaluation of whole-blood use in the prehospital environment is yielding favorable results and challenging the negative dogma currently associated with its use. Management of trauma-related airway and respiratory compromise is evolving, with scope to improve on currently accepted practices. The purpose of this review is to highlight the challenges of treating patients in the prehospital setting and suggest potential solutions. In doing so, we hope to maintain the enthusiasm from people in the field and highlight areas for prehospital specific research and development, so that improved rates of casualty survival will continue.

A hierarchical method for removal of baseline drift from biomedical signals: application in ECG analysis

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.