Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure () signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation () signal. However, the area of research addressing the removal of from signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of from signal utilizing the inherent periodicity and morphological property of the signal. The performance of the proposed technique is tested on signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic signals. The efficiency of the proposed technique in removing from the signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised signal fall under the categories 'very good' as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.

Abstract:Background: The ageing population in developing countries has brought a demographic and an epidemiological transition, with the impact of chronic diseases resulting from life style changes on the health status of the population. Objective: To describ a profile geriatrics patient, specifically to identify epidemiologic, clinical, etiologic and outcome of this group at the department of internal medicine to NNH Patients and method: Medical records of all geriatric patients aged ≥65 years admitted at the department of NNH Between January 2012 and December 2015 were retrieved and reviewed retrospectively. Results: A total of 6074 admissions at the internal medicine department of NNH over three years were reported and 1130 (18, 6%) were geriatrics patients, the average age was 75, 95 years and more than half were men (50,7%). 80 % of patients were in the young old group (65-74 years), 13% in the old group (75-84 years) and 7% in the oldest old group (≥85 years). High blood pressure was the frequent comorbidity (12, 3%) and the most symptoms caused hospitalization were stroke (17, 6%), fevers (16, 5%) and worst health (13, 1%). Frequent illnesses were cardiovascular diseases (38.4%), infections, (19.2%) and endocrine diseases (11%). The average length of hospital stays was 8, 7 days. The mortality rate was 18, 2% and the worst outcomes factors were female sex, frail elderly group in 75 to 84 years and high blood pressure. Conclusion: Chronic diseases were responsible of morbidity and mortality for the majority elderly's patient.

Objectives: Understanding speech in noise can be highly effortful. Decreasing the signal-tonoise ratio (SNR) of speech increases listening effort, but it is relatively unclear if decreasing the level of semantic context does as well. The current study used functional near-infrared spectroscopy (fNIRS) to evaluate two primary hypotheses: (1) listening effort (operationalized as oxygenation of the left lateral PFC) increases as the SNR decreases and (2) listening effort increases as context decreases.Design: Twenty-eight younger adults with normal hearing completed the Revised Speech Perception in Noise (R-SPIN) Test, in which they listened to sentences and reported the final word. These sentences either had an easy SNR (+4 dB) or a hard SNR (-2 dB), and were either low in semantic context (e.g., "Tom could have thought about the sport") or high in context (e.g., "She had to vacuum the rug"). PFC oxygenation was measured throughout using fNIRS.Results: Accuracy on the R-SPIN Test was worse when the SNR was hard than when it was easy, and worse for sentences low in semantic context than high in context. Similarly, oxygenation across the entire PFC (including the left lateral PFC) was greater when the SNR was hard, and left lateral PFC oxygenation was greater when context was low.Conclusions: These results suggest that activation of the left lateral PFC (interpreted here as reflecting listening effort) increases to compensate for acoustic and linguistic challenges. This may reflect the increased engagement of domain-general and domain-specific processes subserved by the DLPFC (e.g., cognitive control) and IFG (e.g., predicting the sensory consequences of articulatory gestures), respectively.

The Esophageal Pressure (Peso) signal can be used to monitor the respiratory mechanics of critically ill patients in the Intensive Care Unit (ICU), and has been successfully used in guiding mechanical ventilation strategies to improve patient outcomes. However, cardiogenic oscillations (CGOs) are a major source of interference, which not only makes it challenging in interpreting the patient’s respiratory mechanics, but can also cause false triggers in the mechanical ventilator resulting in a patient-ventilator asynchrony. In this thesis, we present a Peso enhancement scheme using Ensemble Empirical Mode Decomposition (EEMD) to suppress CGO interference. The proposed method was applied to synthetically generated Peso signals as well as real-world Peso signals from mechanically ventilated ICU patients. The proposed technique has been shown to significantly reduce the amplitude fluctuations caused by CGOs. The technique’s performance has been assessed through Face Validation by our collaborating clinicians, and is found to be suitable in not only suppressing CGO, but also extracting CGO from clinically acquired Peso signals.

<p>Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure (Peso) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the Peso signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation (CGO) signal. However, the area of research addressing the removal of CGO from Peso signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of CGO from Peso signal utilizing the inherent periodicity and morphological property of the Peso signal. The performance of the proposed technique is tested on Peso signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic Peso signals. The efficiency of the proposed technique in removing CGO from the Peso signal is</p> <p>quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised Peso signal fall under the categories ‘very good’ as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.</p>