Assessment and Methods of Nutritional Support during Atropinization in Organophosphate and Carbamate Poisoning Cases

Purpose@#Atropine is an antidote used to relieve muscarinic symptoms in patients with organophosphate and carbamate poisoning. Nutritional support via the enteral nutrition (EN) route might be associated with improved clinical outcomes in critically ill patients. This study examined the administration of nutritional support in patients undergoing atropinization, including methods of supply, outcomes, and complications. @*Methods@#A retrospective observational study was conducted in a tertiary care teaching hospital from 2010 to 2018. Forty-five patients, who were administered with atropine and on mechanical ventilation (MV) due to organophosphate or carbamate poisoning, were enrolled. @*Results@#Nutritional support was initiated on the third day of hospitalization. Thirty-three patients (73.3%) were initially supported using parenteral nutrition (PN). During atropinization, 32 patients (71.1%) received nutritional support via EN (9) or PN (23). There was no obvious reason for not starting EN during atropinization (61.1%). Pneumonia was observed in both patient groups on EN and PN (p=0.049). Patients without nutritional support had a shorter MV duration (p=0.034) than patients with nutritional support.The methods of nutritional support during atropinization did not show differences in the number of hospital days (p=0.711), MV duration (p=0.933), duration of ICU stay (p=0.850), or recovery at discharge (p=0.197). @*Conclusion@#Most patients undergoing atropinization were administered PN without obvious reasons to preclude EN. Nutritional support was not correlated with the treatment outcomes or pneumonia. From these results, it might be possible to choose EN in patients undergoing atropinization, but further studies will be necessary.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

Background@#This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. @*Methods@#A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer.An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. @*Results@#F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. @*Conclusion@#The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Assessment and Methods of Nutritional Support during Atropinization in Organophosphate and Carbamate Poisoning Cases

Purpose@#Atropine is an antidote used to relieve muscarinic symptoms in patients with organophosphate and carbamate poisoning. Nutritional support via the enteral nutrition (EN) route might be associated with improved clinical outcomes in critically ill patients. This study examined the administration of nutritional support in patients undergoing atropinization, including methods of supply, outcomes, and complications. @*Methods@#A retrospective observational study was conducted in a tertiary care teaching hospital from 2010 to 2018. Forty-five patients, who were administered with atropine and on mechanical ventilation (MV) due to organophosphate or carbamate poisoning, were enrolled. @*Results@#Nutritional support was initiated on the third day of hospitalization. Thirty-three patients (73.3%) were initially supported using parenteral nutrition (PN). During atropinization, 32 patients (71.1%) received nutritional support via EN (9) or PN (23). There was no obvious reason for not starting EN during atropinization (61.1%). Pneumonia was observed in both patient groups on EN and PN (p=0.049). Patients without nutritional support had a shorter MV duration (p=0.034) than patients with nutritional support.The methods of nutritional support during atropinization did not show differences in the number of hospital days (p=0.711), MV duration (p=0.933), duration of ICU stay (p=0.850), or recovery at discharge (p=0.197). @*Conclusion@#Most patients undergoing atropinization were administered PN without obvious reasons to preclude EN. Nutritional support was not correlated with the treatment outcomes or pneumonia. From these results, it might be possible to choose EN in patients undergoing atropinization, but further studies will be necessary.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

Background@#This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. @*Methods@#A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer.An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. @*Results@#F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. @*Conclusion@#The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal

BACKGROUND: In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. METHODS: We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database. RESULTS: The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%. CONCLUSION: The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.

Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor

In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.