Comput Math Methods Med - An automated optimal engagement and attention detection system using electrocardiogram.

Tópicos

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Resumo

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

Resumo Limpo

research propos develop monitor system use electrocardiograph ecg fundament physiolog signal analyz predict presenc lack cognit attent individu task execut primari focus studi identifi correl fluctuat level attent implic cardiac rhythm record ecg furthermor electroencephalograph eeg signal also analyz classifi use benchmark comparison ecg analysi sever advanc signal process techniqu implement investig deriv multipl clandestin inform featur physiolog signal decomposit featur extract done use stockwelltransform ecg signal discret wavelet transform dwt use eeg featur appli various machinelearn algorithm produc classif model capabl differenti case person attent person attent present result show detect classif cognit attent use ecg fair compar eeg

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