RESUMO
A number of methods of preventing cardiopulmonary arrest are currently in use. One such method is the alert response system developed by Seoul National University Children's Hospital, which is an early detection and monitoring system for deteriorating patients who are at risk of cardiopulmonary arrest. This system offers an effective means of detecting early warning signs and monitoring deteriorating patients, and its application can reduce rates of cardiopulmonary arrest.
Assuntos
Alarmes Clínicos , Sistemas de Apoio a Decisões Clínicas/organização & administração , Diagnóstico por Computador/métodos , Parada Cardíaca/diagnóstico , Parada Cardíaca/prevenção & controle , Sistemas de Comunicação no Hospital/organização & administração , Monitorização Fisiológica/métodos , Inteligência Artificial , Criança , Diagnóstico Precoce , Humanos , Masculino , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Determining the exact duration of seizure activity is an important factor for predicting the efficacy of electroconvulsive therapy (ECT). In most cases, seizure duration is estimated manually by observing the electroencephalogram (EEG) waveform. In this article, we propose a method based on sample entropy (SampEn) that automatically detects the termination time of an ECT-induced seizure. SampEn decreases during seizure activity and has its smallest value at the boundary of seizure termination. SampEn reflects not only different states of regularity and complexity in the EEG but also changes in EEG amplitude before and after seizure activity. Using SampEn, we can more precisely determine seizure termination time and total seizure duration.
