Case Report: Ictal Central Apnea as First and Overlooked Symptom in Temporal Lobe Seizures

Ictal respiratory changes have been mainly described following generalized tonic-clonic seizures and recently considered to be a biomarker to assess the risk of sudden unexplained death in epilepsy (SUDEP). Nonetheless, modification of respiratory pattern can be related also to focal seizures, especially arising from the temporal lobe. Changes in cardiac function such as tachycardia or bradycardia could be often associated. We report a short case series of four patients with temporal lobe epilepsy admitted to our Epilepsy Monitoring Unit (EMU) presenting with an ictal central apnea as the first clinical manifestation of their seizures. None of these patients was aware of the occurrence of respiratory arrest. Age at onset ranged from 15 to 29 years. One patient had seizures with prolonged central apnea accompanied by a significant decrease in oxygen saturation. Neuroimaging in two patients showed alterations of mesial temporal lobe structures, including the amygdala. Recent neurophysiological studies supported the existence of a cortical network involving the limbic system that modulates downstream brainstem respiratory centers. Monitoring for respiratory changes and oxygen saturation in focal seizures is warranted for their potential value in identifying the epileptogenic zone and for a better understanding of ictal respiratory changes that could potentially define a subgroup of patients with high risk of seizure-related autonomic changes.

14 Diaphragmatic Electrical Activity in Preterm Infants on Non-Invasive High Frequency Oscillatory Ventilation (DEAP-NHFO Study)

Primary Subject area Neonatal-Perinatal Medicine Background Continuous Positive Airway Pressure (CPAP) is a common form of non-invasive respiratory support for preterm infants. Non-invasive high frequency ventilation (NHFOV) is a relatively new method of non-invasive respiratory support. NHFOV is being increasingly utilized in clinical practice in an attempt to prevent intubation and minimize ventilator-induced lung injury in preterm infants. Preliminary studies suggest superiority of NHFOV over CPAP, but little is known about its mechanism of action and its effect on respiratory control in the newborn. We hypothesize that NHFOV reduces respiratory drive and improves ventilation, resulting in decreased patient diaphragm energy expenditure, which can be assessed by measuring the electrical activity of the diaphragm (Edi). Objectives The objective of this study is to compare the effects of non-invasive respiratory support delivered by nasal CPAP versus NHFOV on respiratory pattern, as assessed by the Edi in very low birth weight (VLBW) preterm infants. Design/Methods In a prospective, randomized, crossover study, 20 preterm infants with birth weights ≤1500 g requiring CPAP were randomized to either NHFOV or CPAP for 105 min, followed by crossover to the other method for the same duration. Edi was continuously measured by a feeding catheter with miniaturized sensors embedded in its wall (Maquet, Solna). The general sequence was 15 minutes for acclimation to the mode, 75 minutes for a feed to be completed, followed by 15 minutes for breath-by-breath analyses of neural breathing pattern. Primary outcome was difference in the peak Edi between CPAP and NHFOV. Secondary outcomes included difference in other measures of respiratory drive: neural respiratory rate, neural inspiratory time, diaphragm energy expenditure, transcutaneous pCO2, number of apnea episodes on the Edi, and episodes of clinically significant apnea. Results No significant differences in Edi timing, Edi min, Edi peak, apnea, or CO2 were observed between the two modes of respiratory support. Conclusion In this cohort of VLBW preterm infants, neural respiratory pattern was not significantly different between NHFOV and CPAP. With this baseline information in stable preterm infants, it would now be important to assess whether these results hold true in infants with more severe lung disease, where NHFOV is often used as escalating support from CPAP.

Guidelines on setting the target minute ventilation in Adaptive Support Ventilation

Adaptive Support Ventilation (ASV) is a fully closed loop ventilation where the operator input the desired PEEP, FiO2 and the target minute ventilation (MV) expressed as a percentage according to ideal body weight. The ventilator selects the target respiratory pattern (tidal volume, respiratory rate, and inspiratory time) based on the observed respiratory mechanics. However, there are no published guidelines on settings and adjusting the target MV in different disease states during ASV ventilation. INTELLiVENT-ASV, is the new generation modified algorithm of ASV, has made this issue much easier and simpler as the operator inputs a desired range of the end tidal exhaled carbon dioxide, and oxygen saturation and the algorithm will adjust the minute ventilation percentage as well as PEEP and FiO2 automatically to stay within that range. In this article we describe some evidence-based guidelines on how to set and adjust the target MV in various clinical conditions. Keywords: ASV, INTELLiVENT-ASV, Closed loop ventilation, End tidal CO2, ARDS, COPD, Respiratory failure

Effect Of Diaphragmatic Breathing And Pocketed Lip Breathing Techniques On The Management Of Breathlessness In Patients Ineffective Respiratory Pattern Disorders With Mycobacterium Tuberculosis (Pulmonary Tuberculosis)

Tuberculosis (TB) is a contagious disease caused by the bacterium Mycobacterium Tuberculosis and can spread from one person to another through the sputum droplet air transmission of the patient. Non-pharmacological management of pulmonary tuberculosis includes bed rest, semi-fowler position, comfortable environment, personal hygiene, diaphragm breathing. The purpose of this EBN is to determine the effect of diaphragmatic breathing and pocketed lip breathing on the handling of breathlessness in patients with ineffective respiratory disorders with mycobacterium tuberculosis (pulmonary TB). The implementation strategy used the techniques of literature review obtained through the online search process. The search was conducted via Google Scholar. Pursed lip breathing and diapragmatic breathing are effective in the management of breathlessness in patients with ineffective breathing patterns.

Frequency-Modulated Continuous Wave Radar Respiratory Pattern Detection Technology Based on Multifeature

Respiratory diseases including apnea are often accompanied by abnormal respiratory depth, frequency, and rhythm. If different abnormal respiratory patterns can be detected and recorded, with their depth, frequency, and rhythm analyzed, the detection and diagnosis of respiratory diseases can be achieved. High-frequency millimeter-wave radar (76–81 GHz) has low environmental impact, high accuracy, and small volume, which is more suitable for respiratory signal detection and recognition compared with other contact equipment. In this paper, the experimental platform of frequency-modulated continuous wave (FMCW) radar was built at first, realizing the noncontact measurement of vital signs. Secondly, the energy intensity and threshold of respiration signal during each period were calculated by using the rectangular window, and the accurate judgment of apnea was realized via numerical comparison. Thirdly, the features of respiratory and heart rate signals, the number of peaks and valleys, the difference between peaks and valleys, the average and the standard deviation of normalized short-term energy, and the average and the standard deviation and the minimum of instantaneous frequency, were extracted and analyzed. Finally, support vector machine (SVM) and K-nearest neighbor (KNN) were used to classify the extracted features, and the accuracy was 98.25% and 88.75%, respectively. The classification and recognition of respiratory patterns have been successfully realized.

Non-Contact Respiratory Monitoring Using an RGB Camera for Real-World Applications

Respiratory monitoring is receiving growing interest in different fields of use, ranging from healthcare to occupational settings. Only recently, non-contact measuring systems have been developed to measure the respiratory rate (fR) over time, even in unconstrained environments. Promising methods rely on the analysis of video-frames features recorded from cameras. In this work, a low-cost and unobtrusive measuring system for respiratory pattern monitoring based on the analysis of RGB images recorded from a consumer-grade camera is proposed. The system allows (i) the automatized tracking of the chest movements caused by breathing, (ii) the extraction of the breathing signal from images with methods based on optical flow (FO) and RGB analysis, (iii) the elimination of breathing-unrelated events from the signal, (iv) the identification of possible apneas and, (v) the calculation of fR value every second. Unlike most of the work in the literature, the performances of the system have been tested in an unstructured environment considering user-camera distance and user posture as influencing factors. A total of 24 healthy volunteers were enrolled for the validation tests. Better performances were obtained when the users were in sitting position. FO method outperforms in all conditions. In the fR range 6 to 60 breaths/min (bpm), the FO allows measuring fR values with bias of −0.03 ± 1.38 bpm and −0.02 ± 1.92 bpm when compared to a reference wearable system with the user at 2 and 0.5 m from the camera, respectively.

Machine-learning based feature selection for a non-invasive breathing change detection

Background Chronic Obstructive Pulmonary Disease (COPD) is one of the top 10 causes of death worldwide, representing a major public health problem. Researchers have been looking for new technologies and methods for patient monitoring with the intention of an early identification of acute exacerbation events. Many of these works have been focusing in breathing rate variation, while achieving unsatisfactory sensitivity and/or specificity. This study aims to identify breathing features that better describe respiratory pattern changes in a short-term adjustment of the load-capacity-drive balance, using exercising data. Results Under any tested circumstances, breathing rate alone leads to poor capability of classifying rest and effort periods. The best performances were achieved when using Fourier coefficients or when combining breathing rate with the signal amplitude and/or ARIMA coefficients. Conclusions Breathing rate alone is a quite poor feature in terms of prediction of breathing change and the addition of any of the other proposed features improves the classification power. Thus, the combination of features may be considered for enhancing exacerbation prediction methods based in the breathing signal.Trial Registration : ClinicalTrials NCT03753386. Registered 27 November 2018, https://clinicaltrials.gov/show/NCT03753386

Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

Spinal Muscle Atrophy: Clinical Cases

Spinal muscular atrophy (EBF) is an autosomal recessive neuromuscular disease with genetic inheritance. EBF is classified into: type I - patients have symptoms up to 6 months of age; type II - after 6 months of age, symptoms begin; type III - it starts after 18 months of age. The objective of this study was to clinically characterize two brothers diagnosed with EBF. It is a clinical case study of two individuals, male gender, attended at Clínica Escola de Fisioterapia, at Universidade Estadual do Centro Oeste do Paraná- UNICENTRO, Campus CEDETEG. Selected by eligibility both were diagnosed with spinal muscular atrophy. The physiotherapy stages evaluation was carried out, which consist of anamnesis, functional examination, physical examination and respiratory evaluation. Patient 1, was diagnosed with EBF type IIIb at 16 years old, with reports of falls and weakness mainly in lower limbs, in the evaluation positive Gowers sign, anserine gait, hypotonic, MMSS areflexia and lower limb hyporeflexia, breathing pattern apical were found. Patient 2 was diagnosed with EBF type IIIa at 1 year of age, currently using a wheelchair for locomotion, on physical examination he had scoliosis with right convexity, deformities in the costal grid, decreased muscle strength in the upper limbs and lower limbs, mixed respiratory pattern. From the physical therapy evaluation performed on these patients, it could be noted that the weakness of the respiratory muscles is directly related to the clinical sign presented by both. Keywords: Physiotherapy Specialty. Neuromuscular Diseases. Spinal Muscular Atrophy. ResumoA atrofia muscular espinhal (AME) é uma doença neuromuscular autossômica recessiva com herança genética. A AME é classificada em: tipo I – pacientes apresentam sintomas até 6 meses de idade; tipo II – após os 6 meses de idade inicia-se os sintomas; tipo III – inicia-se após 18 meses de idade. O objetivo desse trabalho foi caracterizar clinicamente dois irmãos com diagnóstico de AME. É um estudo de casos clínicos de dois indivíduos, do gênero masculino, atendidos na Clínica Escola de Fisioterapia, da Universidade Estadual do Centro Oeste do Paraná – UNICENTRO, Campus CEDETEG. Selecionados por elegibilidade ambos diagnosticados com atrofia muscular espinhal. Foram realizadas as etapas da avaliação fisioterapêutica que consistem em anamnese, exame funcional, exame físico e avaliação respiratória. O paciente 1, foi diagnosticado com AME tipo IIIb aos 16 anos, com relatos de quedas e fraqueza principalmente em MMII, na avaliação constatou-se, sinal de Gowers positivo, marcha anserina, hipotônico, arreflexia de MMSS e hiporeflexia de MMII, padrão respiratório apical. O paciente 2 recebeu o diagnóstico de AME tipo IIIa com 01 ano de idade, atualmente utiliza cadeira de rodas para locomoção, no exame físico apresenta escoliose com convexidade a direita, deformidades no gradil costal, força muscular diminuída em MMSS e MMII, padrão respiratório misto. A partir da avaliação fisioterapêutica realizada nesses pacientes, pode-se notar que a fraqueza dos músculos respiratórios está diretamente relacionada com o quadro clinico apresentado por ambos. Palavras-chave: Fisioterapia. Doenças Neuromusculares. Atrofia Muscular Espinhal.