Automated non-contact detection of central apneas using video

Adaptive Remote Sensing Paradigm for Real-Time Alerting of Convulsive Epileptic Seizures

Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic-clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system.

Automatic identification of respiratory events based on nasal airflow and respiratory effort of the chest and abdomen

Objective.Disease may cause changes in an individual's respiratory pattern, which can be measured as parameters for disease evaluation, usually through manually annotated polysomnographic recordings. In this study, a machine learning model based on nasal airflow and respiratory effort of the chest and abdomen is proposed to automatically identify respiratory events, including normal breathing, hypopnea and apnea.Approach.The nasal airflow and chest-abdominal respiratory effort signals were collected by polysomnography (PSG). Time/frequency domain features, fractional Fourier transform features and sample entropy were calculated to obtain feature sets. Features selected through statistical analysis were used as input variables of the machine learning model. The performance of different input combinations on different models was studied and cross-validated.Main results.The dataset included PSG sleep records of 60 patients provided by the Chinese People's Liberation Army General Hospital. The extreme gradient boosting-based model (XGBoost) performed best in several models with an accuracy of 0.807 and a F1 score of 0.807, depending on the combination of nasal airflow and two respiratory effort signals. The precision for normal breathing, hypopnea and apnea events were 0.764, 0.789 and 0.871, respectively. In addition, the recall scores were 0.833, 0.768 and 0.823 for normal breathing, hypopnea and apnea events, respectively. Moreover, it was found that the standard deviation and kurtosis of nasal airflow were the most important features of the respiratory event detection model.Significance.Since nasal airflow and respiratory effort of the chest and abdomen contain the characteristics of respiratory events, their combined use can improve the classification performance for identification of respiratory events. With this method, respiratory events can be automatically detected and labeled from the PSG records, which can be used to screen for patients with sleep apnea-hypopnea syndrome.