MATERIAL AND METHODS: A prospective, quasi-experimental physiological study. Selected healthy subjects were observed electrocardiographically for 60 s continuously in three equal phases of 20 s each - baseline phase, nasoendoscopic phase, and recovery phase (post-nasoendoscopy). Heart rate fluctuations were charted, followed by identification of a positive nasocardiac reflex group of subjects and a negative group. Analyses against multiple variables were done.
RESULTS: A total of 53 subjects were analysed. Heart rate during the baseline phase was 81.0 ± 9.9, nasoendoscopic phase was 72.7 ± 10.1, and recovery phase was 75.2 ± 9.6. Sixteen subjects (30.2%) had a positive nasocardiac reflex, and they remained in sinus rhythm with no occurrences of skipped beats, atrioventricular blocks or asystoles. One subject (1.9%) developed temporary ectopic premature ventricular contractions after nasoendoscopy. No variables were found affecting the incidence of a nasocardiac reflex in our study.
CONCLUSIONS: The pattern of heart rate dynamics was consistent as heart rates drop rapidly upon endoscope insertion and recover in some measure after its withdrawal. Although all our subjects remained asymptomatic, clinicians should not overlook the risks of a severe nasocardiac reflex when performing nasoendoscopy. We recommend that electrical cardiac monitoring be part of the management of vasovagal responses during in-office endonasal procedures.
METHODS: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.
RESULTS: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.
CONCLUSIONS: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.