Music therapy with adult burn patients in the intensive care unit: short-term analysis of electrophysiological signals during music-assisted relaxation.

Burn patients often face elevated pain, anxiety, and depression levels. Music therapy adds to integrative care in burn patients, but research including electrophysiological measures is limited. This study reports electrophysiological signals analysis during Music-Assisted Relaxation (MAR) with burn patients in the Intensive Care Unit (ICU). This study is a sub-analysis of an ongoing trial of music therapy with burn patients in the ICU. Electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) were recorded during MAR with nine burn patients. Additionally, background pain levels (VAS) and anxiety and depression levels (HADS) were assessed. EEG oscillation power showed statistically significant changes in the delta (p < 0.05), theta (p = 0.01), beta (p < 0.05), and alpha (p = 0.05) bands during music therapy. Heart rate variability tachograms high-frequencies increased (p = 0.014), and low-frequencies decreased (p = 0.046). Facial EMG mean frequency decreased (p = 0.01). VAS and HADS scores decreased - 0.76 (p = 0.4) and - 3.375 points (p = 0.37) respectively. Our results indicate parasympathetic system activity, attention shifts, reduced muscle tone, and a relaxed state of mind during MAR. This hints at potential mechanisms of music therapy but needs to be confirmed in larger studies. Electrophysiological changes during music therapy highlight its clinical relevance as a complementary treatment for ICU burn patients.Trial registration: Clinicaltrials.gov (NCT04571255). Registered September 24th, 2020. https//classic.clinicaltrials.gov/ct2/show/NCT04571255.

A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data.

The electrocardiogram records the heart's electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.

Ventricular tachycardia or artifact?

Electrocardiographic artifacts are extracardiac signals that may alter the electrocardiogram (ECG) generating false diagnoses. These artifacts may simulate pathologies on ECG's in healthy patients and result in long-term unnecessary or even deleterious treatments. On the other hand, to consider an arrhythmia as an artifact, may carry even worse consequences.

Análise comparativa de desempenho das transformadas Wavelet e Hilbert na detecção do QRS em ECG / Performance comparison analysis of Wavelet and Hilbert transforms for QRS detection in ECG

O processo de detecção do complexo QRS é o primeiro passo de um processo de extração de parâmetros do sinal eletrocardiograma (ECG) em sistemas de auxílio ao diagnóstico médico. O presente trabalho apresenta resultados detalhados de comparação da aplicação de duas transformadas matemáticas, Wavelet e Hilbert, em um algoritmo de detecção de QRS em termos de taxas de detecções corretas (sensibilidade e preditividade positiva) e de uma medida de frequência de recorrência a processos de filtragem (pré-processamento). Uma abordagem inovadora é implementada, na qual as rotinas de filtragem são inseridas dentro do estágio de decisão, ou seja, é realizada a supressão da etapa de pré-processamento. As transformadas são aplicadas no algoritmo, que é baseado em um limiar adaptativo, com o objetivo de realçar, apenas quando necessário, os picos (pontos fiduciais)do QRS. Em uma primeira abordagem, apenas a transformada Wavelet é utilizada neste realce e, numa segunda abordagem, a transformada de Hilbert é inserida em série à aplicação da Wavelet em dois possíveis arranjos. São realizados experimentos dos algoritmos sobre os exames da base de dados Arrhythmia Database, pertencente ao conjunto de bases de dados do MIT-BIH. É composta por 48 gravações de ECG com duração de trinta minutos, amostrados a uma frequência de 360 Hz com resolução de 4,88 μV sobre uma faixa de variação de 10 mV. Ao todo, contabilizam-se 109.662 complexos QRS. Taxas de 98,85% de sensibilidade e 95,10% de preditividade positiva são obtidas com a aplicação exclusiva da transformada Wavelet, enquanto que 98,89% de sensibilidade e 98,52% de preditividade positiva são obtidas com aaplicação em série das transformadas Wavelet e de Hilbert.

The process of QRS detection is the first stage of a greater process: the feature extraction in the electrocardiogram (ECG). This work presents detailed results on the performance of two mathematical transforms, Hilbert and Wavelet, which are applied in QRS detection. The evaluation parameters are the detection rates and a measure of frequency of recurrence to filtering processes. An innovative approach is implemented: the filtering routines are inserted in the decision stage, i.e. the preprocessing stage is removed. The algorithm is based on adaptive threshold technique and the two transforms are applied in order to emphasize, only when necessary, the QRS fiducial points. In a first approach, only the Wavelet transform is applied, and in a second approach, the Hilbert transform is inserted before the Wavelet transform or after it. We evaluate these approaches on the well-known MIT-BIH Arrhythmia Database. It contains 48 half-hour recordings of annotated ECG with a sampling rate of 360 Hz and 4.88 μV resolution over a 10 mV range, totalizing 109,662 QRS complexes. Sensitivity rates of 98.85% and 98.89% are respectively attained when the Wavelet transform is applied in the filtering processes and both Hilbert and Wavelet transforms are applied. Predictability rates of 95.10% and 98.52% are also attained respectively using Wavelet transform and the simultaneous application of Hilbert and Wavelet transforms in the filtering processes.