@artikel{Adib2018,
tajuk = {Terjemahan dan pengesahan skala kepuasan ibu bapa versi bahasa melayu (Pss-m) untuk penilaian kepuasan penjaga terhadap perkhidmatan penjagaan kesihatan untuk kanak-kanak yang mengalami gangguan spektrum autisme},
pengarang = {N A N Adib and M I Ibrahim and A A Rahman and R S Bakar and N A Yahaya and S Hussin and W N Arifin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056090545&doi=10.3390/ijerph15112455&rakan kongsi = 40&md5=53650806d46343cc3e95c9b30442f79c},
doi = {10.3390/ijerph15112455},
terbitan = {16617827},
tahun = {2018},
tarikh = {2018-01-01},
jurnal = {Jurnal Antarabangsa Penyelidikan Alam Sekitar dan Kesihatan Awam},
isi padu = {15},
nombor = {11},
penerbit = {MDPI AG},
abstrak = {Latar belakang: Skala Kepuasan Ibu Bapa versi Bahasa Melayu (PSS-M) diperlukan untuk menyiasat faktor-faktor yang menyumbang kepada kepuasan pengasuh Melayu terhadap pengurusan penjagaan kesihatan untuk kanak-kanak yang mengalami gangguan spektrum autisme. (ASD). Matlamat kajian adalah untuk menterjemah dan mengesahkan soal selidik untuk menilai kepuasan penjaga terhadap perkhidmatan penjagaan kesihatan. Kaedah: Kajian keratan rentas telah dijalankan di kalangan 110 penjaga kanak-kanak dengan ASD berumur antara 2 dan 17 tahun yang menerima rawatan di dua pusat jagaan tinggi di Kelantan. Kebenaran untuk menggunakan borang soal selidik PSS versi asal telah diperolehi. Versi Inggeris asal PSS telah diterjemahkan ke dalam versi Bahasa Melayu berikutan 10 langkah yang dicadangkan oleh garis panduan yang ditetapkan. Pra-ujian PSS telah dijalankan dengan 30 penjaga sebelum analisis faktor pengesahan (CFA) ditubuhkan menggunakan 110 penjaga. Mereka diminta menilai kefahaman mereka terhadap soal selidik. Soal selidik satu dimensi terdiri daripada 11 barang, termasuk sikap kakitangan, ketersediaan kakitangan, sokongan, dan suka menolong. Skala Likert 5 mata memberikan penilaian daripada 1 (sangat tidak setuju) ke 5 (sangat bersetuju). Analisis faktor pengesahan dilakukan menggunakan penganggar kemungkinan maksimum yang teguh. Keputusan: Analisis menunjukkan data kesesuaian model dengan kebolehpercayaan yang baik. Kesimpulannya: PSS-M menunjukkan kecergasan model keseluruhan berdasarkan indeks tertentu, dengan kesahan konstruk yang baik dan kebolehpercayaan mutlak yang sangat baik untuk menentukan tahap kepuasan penjaga kanak-kanak dengan ASD berkenaan dengan perkhidmatan penjagaan kesihatan. © 2018, MDPI AG. Hak cipta terpelihara.},
nota = {dipetik oleh 2},
kata kunci = {Dewasa, Artikel, Autisme, Gangguan Spektrum Autisme, Pengasuh, Penjagaan Kanak-kanak, Hubungan Ibu Bapa Anak, Anak-anak, Kajian Lintas Bahagian, Analisis Faktor, Perempuan, Garis panduan, Sikap Kakitangan Kesihatan, Perkhidmatan kesihatan, Pekerja kesihatan, Manusia, Kelantan, Fungsi Kemungkinan, Skala Likert, Malaysia, Lelaki, Analisis Kemungkinan Maksimum, Kesihatan mental, Perkhidmatan Kesihatan Mental, Ibu bapa, Skala Kepuasan Ibu Bapa Versi Bahasa Melayu, Kepuasan Peribadi, Garis Panduan Amalan, Skala Penarafan Psikologi, Psikologi, Penerbitan, Soal selidik, Kebolehulangan, Kebolehulangan Keputusan, Kepuasan hati, Model Statistik, Statistik, Tinjauan, Pusat Jagaan Tertiari, Terjemahan, Kajian Pengesahan, West Malaysia},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Hasan201755,
tajuk = {Analisis daya tindak balas tanah tiga dimensi semasa berjalan pada kanak-kanak dengan gangguan spektrum autisme},
pengarang = {C Z C Hasan and R Jailani and N Md Tahir and S Ilias},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015640386&doi = 10.1016% 2fj.ridd.2017.02.015&rakan kongsi = 40&md5=d6a9839cda7f62bcce9bdcca33d3d33b},
doi = {10.1016/j.ridd.2017.02.015},
terbitan = {08914222},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {Penyelidikan Ketidakupayaan Pembangunan},
isi padu = {66},
halaman = {55-63},
penerbit = {Elsevier Inc.},
abstrak = {Maklumat minimum diketahui tentang tiga dimensi (3D) daya tindak balas tanah (GRF) mengenai corak gaya berjalan individu yang mengalami gangguan spektrum autisme (ASD). Tujuan kajian ini adalah untuk menyiasat sama ada komponen GRF 3D berbeza secara signifikan antara kanak-kanak dengan ASD dan kawalan rakan sebaya.. 15 kanak-kanak dengan ASD dan 25 biasanya berkembang (TD) kanak-kanak telah mengambil bahagian dalam kajian ini. Dua plat daya digunakan untuk mengukur data GRF 3D semasa berjalan. Teknik parameterisasi siri masa telah digunakan untuk mengekstrak 17 ciri diskret daripada bentuk gelombang GRF 3D. Dengan menggunakan ujian-t bebas dan ujian Mann-Whitney U, perbezaan yang ketara (hlm < 0.05) between the ASD and TD groups were found for four GRF features. Children with ASD demonstrated higher maximum braking force, lower relative time to maximum braking force, and lower relative time to zero force during mid-stance. Children with ASD were also found to have reduced the second peak of vertical GRF in the terminal stance. These major findings suggest that children with ASD experience significant difficulties in supporting their body weight and endure gait instability during the stance phase. The findings of this research are useful to both clinicians and parents who wish to provide these children with appropriate treatments and rehabilitation programs. © 2017 Elsevier Ltd},
nota = {dipetik oleh 8},
kata kunci = {Taburan Umur, Artikel, Autisme, Gangguan Spektrum Autisme, Fenomena Biomekanikal, Biomekanik, Keseimbangan Badan, Tinggi Badan, Berat badan, Berat badan, Anak-anak, Artikel Klinikal, Kajian Terkawal, Penilaian Penyakit, Perempuan, Langkah, Analisis Gait, Gangguan Gaya Berjalan, Angkatan Tindak Balas Tanah, Manusia, Pengimejan, Panjang Kaki, Malaysia, Lelaki, Pemeriksaan Neurologi, Patofisiologi, Fisiologi, Keseimbangan Postur, Prosedur, Psikologi, Statistik, Tiga Dimensi, Pengimejan Tiga Dimensi, Daya Tindak Balas Tanah Tiga Dimensi, berjalan},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Hasan201774,
tajuk = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder},
pengarang = {C Z C Hasan and R Jailani and N Md Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&rakan kongsi = 40&md5=6f2ffe7c2f5daf9fd02d4456acb94438},
terbitan = {19984510},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {International Journal of Biology and Biomedical Engineering},
isi padu = {11},
halaman = {74-79},
penerbit = {North Atlantic University Union NAUN},
abstrak = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% ketepatan, 93.3% kepekaan, dan 90.0% kekhususan. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. Hak cipta terpelihara.},
nota = {dipetik oleh 1},
kata kunci = {Artikel, Artificial Neural Network, Autisme, Tinggi Badan, Berat badan, Anak-anak, Artikel Klinikal, Kajian Terkawal, Analisis Diskriminan, Early Diagnosis, Perempuan, Langkah, Analisis Gait, Gangguan Gaya Berjalan, Manusia, Belajar, Lelaki, Pediatrics, Budak sekolah, Statistical Analysis, Statistik, Time Series Analysis},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Hameed2017,
tajuk = {Pemilihan dan klasifikasi ekspresi gen dalam gangguan autisme: Penggunaan gabungan penapis statistik dan algoritma GBPSO-SVM},
pengarang = {S S Hameed dan R Hassan dan F F Muhammad},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033361187&doi=10.1371/journal.pone.0187371&rakan kongsi = 40&md5=f9260d41165145f229a3cf157699635e},
doi = {10.1371/jurnal.pone.0187371},
terbitan = {19326203},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {PLoS SATU},
isi padu = {12},
nombor = {11},
penerbit = {Perpustakaan Awam Sains},
abstrak = {Dalam kerja ini, ekspresi gen dalam gangguan spektrum autisme (ASD) dianalisis dengan matlamat untuk memilih gen yang paling dikaitkan dan melaksanakan pengelasan. Objektif ini dicapai dengan menggunakan gabungan pelbagai penapis statistik dan mesin vektor sokongan pengoptimuman zarah binari geometri berasaskan pembalut (GBPSO-SVM) algoritma. Penggunaan penapis yang berbeza telah diserlahkan dengan memasukkan kriteria nisbah min dan median untuk membuang gen yang sangat serupa. Keputusan menunjukkan bahawa gen yang paling diskriminatif yang dikenal pasti dalam langkah pemilihan pertama dan terakhir termasuk kehadiran gen berulang. (CAPS2), yang ditugaskan sebagai gen yang paling berkaitan dengan risiko ASD. Subset gen gabungan yang dipilih oleh algoritma GBPSO-SVM dapat meningkatkan ketepatan klasifikasi. © 2017 Hameed et al. Ini ialah artikel akses terbuka yang diedarkan di bawah syarat Lesen Atribusi Creative Commons, yang membenarkan penggunaan tanpa had, pengedaran, dan pembiakan dalam mana-mana medium, dengan syarat penulis dan sumber asal dikreditkan.},
nota = {dipetik oleh 11},
kata kunci = {Ketepatan, Algoritma, Artikel, Autisme, Gangguan Spektrum Autisme, Gen CAPS2, Pengelasan (maklumat), Pengelas, Kajian Eksperimen, Gen, Ekspresi Gen, Pengenalan Gen, Persatuan Genetik, Prosedur Genetik, Risiko Genetik, Genetik, Algoritma Mesin Vektor Sokongan Pengoptimuman Zarah Perduaan Perduaan Geometri, Manusia, Penilaian risiko, Penyeragaman, Penapis Statistik, Parameter Statistik, Statistik, Mesin Vektor Sokongan},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{MohdRoffeei2015375,
tajuk = {Seeking social support on Facebook for children with Autism Spectrum Disorders (ASDs)},
pengarang = {S H Mohd Roffeei and N Abdullah and S K R Basar},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924517643&doi=10.1016%2fj.ijmedinf.2015.01.015&rakan kongsi = 40&md5=7296825093cbf87388b5b65023e59371},
doi = {10.1016/j.ijmedinf.2015.01.015},
terbitan = {13865056},
tahun = {2015},
tarikh = {2015-01-01},
jurnal = {International Journal of Medical Informatics},
isi padu = {84},
nombor = {5},
halaman = {375-385},
penerbit = {Elsevier Ireland Ltd},
abstrak = {Tujuan: This study examined the types of social support messages exchanged between parents and/or caregivers of children with Autism Spectrum Disorders (ASDs) who communicate via Facebook (FB); it studies two autism support groups: Autism Malaysia (PAGI) and Autism Children Club (ACA). Kaedah: Sejumlah 3637 messages including both postings (381) and comments (3256) were gathered from August to November 2013. The study employed a deductive content-analysis approach. The qualitative data were analyzed for social support themes adapted from the Social Support Behavior Code (SSBC). Before collecting the data, email was sent to the FB groups' moderators to gain formal consent from the members. Result: The finding indicated that the highest percentage of messages offered dealt with Informational support (30.7%) followed by Emotional support (27.8%). Network and Esteem support messages were responsible for 20.97% dan 20.2%, masing-masing. Tangible Assistance was the least frequent category (0.4%). A majority of these messages discussed and addressed challenges and difficulties associated with caring and raising ASD children, as well as issues such as children's social lives and self-care routines. Kesimpulannya: Understandings of how FB is used to seek social support could impact supporting and maintaining effective communication among parents and/or caregivers of children with ASDs. This information could also improve approaches used by health professionals in developing, improving and evaluating social support systems for parents/caregivers. © 2015 Elsevier Ireland Ltd.},
nota = {dipetik oleh 43},
kata kunci = {Artikel, ASD, Autisme, Gangguan Spektrum Autisme, Pengasuh, Anak-anak, Consumer Health Information, Content Analysis, Penyakit, E-mail, Facebook, Keluarga, Friendship, Manusia, Internet, Ibu bapa, Patient Referral, Jurnal Keutamaan, Psikologi, Analisis Kualitatif, Self Esteem, Media sosial, Rangkaian sosial, Sokongan Sosial, Statistik, Support Group, Telemedicine, Utilization},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Selvaraj2013,
tajuk = {Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst},
pengarang = {J Selvaraj and M Murugappan and K Wan and S Yaacob},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879017985&doi=10.1186%2f1475-925X-12-44&rakan kongsi = 40&md5=18c5309ac9f3017f455480f1ff732a30},
doi = {10.1186/1475-925X-12-44},
terbitan = {1475925X},
tahun = {2013},
tarikh = {2013-01-01},
jurnal = {BioMedical Engineering Online},
isi padu = {12},
nombor = {1},
penerbit = {BioMed Central Ltd.},
abstrak = {Latar belakang: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. Walau bagaimanapun, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.Methods: Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.Results: Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (hlm < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.Conclusions: The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. © 2013 Selvaraj et al.; licensee BioMed Central Ltd.},
nota = {dipetik oleh 42},
kata kunci = {Remaja, Dewasa, Aged, Artikel, Audio-Visual Stimulus, Autonomous Nervous Systems, Anak-anak, Ketepatan Pengelasan, Computer Based Training, Computer-Assisted, Electrocardiogram Signal, Electrocardiography, Emosi, Perempuan, Fuzzy K-nearest Neighbor, Higher-Order Statistic (HOS), Manusia, Kecacatan Intelektual, Sistem Komputer Interaktif, Metodologi, Pertengahan umur, Nonlinear Dynamics, Nonlinear System, Prosedur, Real Time Systems, Pemprosesan isyarat, Statistik, Dewasa Muda},
pubstate = {diterbitkan},
tppubtype = {artikel}
}