Analisis Fitur HRV pNN50 pada Sinyal Psikofisiologis Marah Manusia

Affective Computing dan Affective medicine dapat menjadi bidang yang menggabungkan teknik komputasi, ilmu kesehatan dan psikologi. Bidang ini dikembangkan untuk mempelajari dan mengkomputasi psikologi manusia dengan menggunakan metode matematika. Dalam paper ini, kami meneliti sinyal psikofisiologis Marah Manusia dengan menggunakan fitur pNN50 Heart Rate Variability. Dalam penelitian ini kami menggunakan sensor EKG untuk merekan reaksi jantung manusia terhadap stimuli video marah yang dipertunjukkan ke mereka. Sinyal tersebut akan dianalisis dengan menggunakan aplikasi kubiosHRV untuk mendapat nilai pNN50 dari masing-masing partisipan. Hasil penelitina ini menunjukkan bahwa ada perbedaan nilai pNN50 sebelum dan sesudah mendapatkan Stimuli Video. Hal ini menunjukkan bahwa pNN50 dapat digunakan sebagai fitur untuk membedakan sinyal jantung manusia pada saat marah dan normal

Comparing features from ECG pattern and HRV analysis for emotion recognition system

Abstract We propose new features for emotion recognition from short ECG signals. The features represent the statistical distribution of dominant frequencies, calculated using spectrogram analysis of intrinsic mode function after applying the bivariate empirical mode decomposition to ECG. KNN was used to classify emotions in valence and arousal for a 3-class problem (low-medium-high). Using ECG from the Mahnob-HCI database, the average accuracies for valence and arousal were 55.8% and 59.7% respectively with 10-fold cross validation. The accuracies using features from standard Heart Rate Variability analysis were 42.6% and 47.7% for valence and arousal respectively for the 3-class problem. These features were also tested using subject-independent validation, achieving an accuracy of 59.2% for valence and 58.7% for arousal. The proposed features also showed better performance compared to features based on statistical distribution of instantaneous frequency, calculated using Hilbert transform of intrinsic mode function after applying standard empirical mode decomposition and bivariate empirical mode decomposition to ECG. We conclude that the proposed features offer a promising approach to emotion recognition based on short ECG signals. The proposed features could be potentially used also in applications in which it is important to detect quickly any changes in emotional state