Ujian - National Autism Resource Centre (NARC)

Hariharan, M; Sindhu, R; Vijean, V; Yazid, H; Nadarajaw, T; Yaacob, S; Polat, K

Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification Artikel Jurnal

Computer Methods and Programs in Biomedicine, 155 , hlm. 39-51, 2018, ISSN: 01692607, (dipetik oleh 21).

Abstrak | Pautan | BibTeX | Tag: Accidents, Algoritma, Artikel, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Pengelasan (maklumat), Classification Algorithm, Pengelas, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Pengekstrakan, Extreme Learning Machine, Factual, Factual Database, Pengekstrakan Ciri, Kaedah Pemilihan Ciri, Fuzzy System, Hearing Impairment, Manusia, Kelaparan, Bayi, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Belajar, Linear Predictive Coding, Pembelajaran Mesin, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Pekali Cepstral Frekuensi Mel, Multi-Class Classification, Rangkaian Neural, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Patofisiologi, Prematurity, Kebolehulangan, Kebolehulangan Keputusan, Pemprosesan isyarat, Pengenalan suara, Wavelet Analysis, Wavelet Packet, Paket Wavelet Berubah

@artikel{Hariharan201839,
tajuk = {Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification},
pengarang = {M Hariharan and R Sindhu and V Vijean and H Yazid and T Nadarajaw and S Yaacob and K Polat},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036611215&doi=10.1016%2fj.cmpb.2017.11.021&rakan kongsi = 40&md5=1f3b17817b00f07cadad6eb61c0f4bf9},
doi = {10.1016/j.cmpb.2017.11.021},
terbitan = {01692607},
tahun = {2018},
tarikh = {2018-01-01},
jurnal = {Computer Methods and Programs in Biomedicine},
isi padu = {155},
halaman = {39-51},
penerbit = {Elsevier Ireland Ltd},
abstrak = {Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. Dalam kerja ini, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) dan 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) dan 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 ciri-ciri), Linear Predictive Coding (LPC) based cepstral features (56 ciri-ciri), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 ciri-ciri). The combined feature set consists of 568 ciri-ciri. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Akhirnya, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V.},
nota = {dipetik oleh 21},
kata kunci = {Accidents, Algoritma, Artikel, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Pengelasan (maklumat), Classification Algorithm, Pengelas, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Pengekstrakan, Extreme Learning Machine, Factual, Factual Database, Pengekstrakan Ciri, Kaedah Pemilihan Ciri, Fuzzy System, Hearing Impairment, Manusia, Kelaparan, Bayi, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Belajar, Linear Predictive Coding, Pembelajaran Mesin, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Pekali Cepstral Frekuensi Mel, Multi-Class Classification, Rangkaian Neural, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Patofisiologi, Prematurity, Kebolehulangan, Kebolehulangan Keputusan, Pemprosesan isyarat, Pengenalan suara, Wavelet Analysis, Wavelet Packet, Paket Wavelet Berubah},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

Tutup

Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. Dalam kerja ini, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) dan 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) dan 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 ciri-ciri), Linear Predictive Coding (LPC) based cepstral features (56 ciri-ciri), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 ciri-ciri). The combined feature set consists of 568 ciri-ciri. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Akhirnya, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V.

Tutup

Sudirman, R; Hussin, S S; Airij, A G; Hai, C Z

Penunjuk profil untuk kanak-kanak autistik menggunakan potensi biosignal EEG untuk tugas deria Persidangan

Institut Jurutera Elektrik dan Elektronik Inc., 2018, ISBN: 9781538612774, (dipetik oleh 0).

Abstrak | Pautan | BibTeX | Tag: Kanak-kanak Autistik, Pemprosesan Isyarat Bioperubatan, Otak, Kanak-kanak dengan Autisme, Elektroensefalografi, Elektrofisiologi, Anggaran Entropi, Analisis Komponen Bebas, MATLAB, Rangkaian Neural, Masalah Neurologi, Analisis Deria, Profil Deria, Rangsangan Deria, Paket Wavelet Berubah

@ persidangan{Sudirman2018136,
tajuk = {Penunjuk profil untuk kanak-kanak autistik menggunakan potensi biosignal EEG untuk tugas deria},
pengarang = {R Sudirman dan SS Hussin dan A G Airij dan C Z Hai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058032461&doi = 10.1109% 2fICBAPS.2018.8527403&rakan kongsi = 40&md5=30dbb1596f4a0529332713c087bd788d},
doi = {10.1109/ICBAPS.2018.8527403},
isbn = {9781538612774},
tahun = {2018},
tarikh = {2018-01-01},
jurnal = {2nd Persidangan Antarabangsa mengenai Analisis BioSignal, Pemprosesan dan Sistem, ICBAPS 2018},
halaman = {136-141},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Elektroensefalografi (LIHAT) ialah ukuran voltan yang disebabkan oleh aktiviti saraf di dalam otak. EEG ialah alat yang disyorkan untuk mendiagnosis masalah neurologi kerana ia tidak invasif dan boleh direkodkan dalam tempoh masa yang lebih lama.. Kanak-kanak Autism Spectrum Disorder (ASD) mengalami kesukaran untuk meluahkan emosi mereka kerana ketidakupayaan mereka memproses maklumat yang betul dalam otak. Oleh itu, penyelidikan ini bertujuan untuk membina profil deria dengan bantuan potensi biosignal EEG untuk membezakan antara tindak balas deria yang berbeza. Isyarat EEG yang diperoleh dalam penyelidikan ini mengenal pasti keadaan emosi yang berbeza seperti berfikiran positif atau super-pembelajaran dan relaksasi ringan dan berada dalam julat frekuensi 8-12 Hertz. 64 kanak-kanak mengambil bahagian dalam penyelidikan ini antaranya 34 adalah kanak-kanak dengan ASD dan 30 adalah kanak-kanak biasa. Data EEG dikod semula manakala semua kanak-kanak dibekalkan dengan vestibular, visual, bunyi, rasa dan rangsangan deria vestibular. Data EEG mentah telah ditapis dengan bantuan analisis komponen bebas (ICA) menggunakan transformasi wavelet dan perisian EEGLAB. Nanti, untuk membina profil deria, penghampiran entropi, min dan sisihan piawai telah diekstrak daripada isyarat EEG yang ditapis. Bersama-sama dengan itu, data EEG yang ditapis juga disalurkan ke rangkaian saraf (NN) algoritma yang telah dilaksanakan dalam MATLAB. Keputusan daripada isyarat EEG yang diperoleh menggambarkan bahawa semasa fasa rangsangan deria, respons semua kanak-kanak autisme berada dalam keadaan tidak stabil. Penemuan ini akan melengkapkan dan membantu strategi pembelajaran mereka pada masa hadapan. © 2018 IEEE.},
nota = {dipetik oleh 0},
kata kunci = {Kanak-kanak Autistik, Pemprosesan Isyarat Bioperubatan, Otak, Kanak-kanak dengan Autisme, Elektroensefalografi, Elektrofisiologi, Anggaran Entropi, Analisis Komponen Bebas, MATLAB, Rangkaian Neural, Masalah Neurologi, Analisis Deria, Profil Deria, Rangsangan Deria, Paket Wavelet Berubah},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

Tutup

Sudirman, R; Hussin, S S

Sensory responses of autism via electroencephalography for Sensory Profile Persidangan

Institut Jurutera Elektrik dan Elektronik Inc., 2014, ISBN: 9781479956869, (dipetik oleh 3).

Abstrak | Pautan | BibTeX | Tag: Autisme, Discrete Wavelet Transforms, Penyakit, Elektroensefalografi, Elektrofisiologi, Analisis Komponen Bebas, International System, Belajar, Analisis Deria, Profil Deria, Sensory Profiling, Rangsangan Deria, Pemprosesan isyarat, Standard Deviation, Paket Wavelet Berubah

@ persidangan{Sudirman2014626,
tajuk = {Sensory responses of autism via electroencephalography for Sensory Profile},
pengarang = {R Sudirman and S S Hussin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946435600&doi=10.1109%2fICCSCE.2014.7072794&rakan kongsi = 40&md5=3e6f1cfe19eae4fad359d2493aebd7e0},
doi = {10.1109/ICCSCE.2014.7072794},
isbn = {9781479956869},
tahun = {2014},
tarikh = {2014-01-01},
jurnal = {Prosiding - 4th IEEE International Conference on Control System, Pengkomputeran dan Kejuruteraan, ICCSCE 2014},
halaman = {626-631},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {The aim of this study is to investigate the brain signals of autism children through electroencephalography (LIHAT) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, rasa, sentuhan, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE.},
nota = {dipetik oleh 3},
kata kunci = {Autisme, Discrete Wavelet Transforms, Penyakit, Elektroensefalografi, Elektrofisiologi, Analisis Komponen Bebas, International System, Belajar, Analisis Deria, Profil Deria, Sensory Profiling, Rangsangan Deria, Pemprosesan isyarat, Standard Deviation, Paket Wavelet Berubah},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

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