ABSTRACT
The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of sleep instability. The CAP can examine various sleep-related disorders. Certain short events (A and B phases) manifest related to a specific physiological process or pathology during non-rapid eye movement (NREM) sleep. These phases unexpectedly modify EEG oscillations; hence, manual detection is challenging. Therefore, it is highly desirable to have an automated system for detecting the A-phases (AP). Deep convolution neural networks (CNN) have shown high performance in various healthcare applications. A variant of the deep neural network called the Wavelet Scattering Network (WSN) has been used to overcome the specific limitations of CNN, such as the need for a large amount of data to train the model. WSN is an optimized network that can learn features that help discriminate patterns hidden inside signals. Also, WSNs are invariant to local perturbations, making the network significantly more reliable and effective. It can also help improve performance on tasks where data is minimal. In this study, we proposed a novel WSN-based CAPSCNet to automatically detect AP using EEG signals. Seven dataset variants of cyclic alternating pattern (CAP) sleep cohort is employed for this study. Two electroencephalograms (EEG) derivations, namely: C4-A1 and F4-C4, are used to develop the CAPSCNet. The model is examined using healthy subjects and patients tormented by six different sleep disorders, namely: sleep-disordered breathing (SDB), insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, periodic leg movement disorder (PLM) and rapid eye movement behavior disorder (RBD) subjects. Several different machine-learning algorithms were used to classify the features obtained from the WSN. The proposed CAPSCNet has achieved the highest average classification accuracy of 83.4% using a trilayered neural network classifier for the healthy data variant. The proposed CAPSCNet is efficient and computationally faster.
Subject(s)
Sleep Apnea Syndromes , Sleep Wake Disorders , Humans , Sleep Stages/physiology , Polysomnography , Sleep/physiology , ElectroencephalographyABSTRACT
BACKGROUND: Spinal anesthetic-induced hypotension is the most worrisome complication for patients undergoing cesarean section under spinal anesthesia. The present study compares norepinephrine and phenylephrine bolus for the treatment of hypotension during spinal anesthesia for cesarean section. METHODS: One hundred twenty- six women aged between 22 and 40 years with singleton pregnancy classified to the American Society of Anesthesiologists (ASA) physical class I and II posted for elective cesarean section under spinal anesthesia were randomly divided into two groups of 63 each. Group I patients received phenylephrine 50 mcg (microgram) as an intravenous bolus, and Group II received 4 mcg of norepinephrine as an intravenous bolus to treat spinal hypotension. RESULTS: On comparing the demographic data of the patients in terms of age, weight, height, ASA Grade, level of block and surgery time no significant differences were found between the groups. Similarly, the fetal parameters were found to be not significantly different between the groups. However, the number of bolus doses of vasopressors required for the treatment of spinal-induced hypotension was significantly reduced in Group II (p=0.02). The frequency of bradycardia was found to be higher in patients who were given phenylephrine as compared to patients administered noradrenaline boluses (p=0.03). Five (7.93%) patients had shivering in Group I, while similar episodes were observed in 10 (15.87%) patients (p=0.05). Moreover, no significant difference was observed in comparing the heart rate and mean arterial pressure between the groups. CONCLUSION: Intermittent boluses of norepinephrine are found to be effective in the management of spinalinduced hypotension during caesarean section.
