Cognitive Behavior Therapy for Generalized Anxiety Disorder: A Case Study of Arrhythmia Patient

Dewinta, Putri; Menaldi, Adhityawarman

doi:10.21512/humaniora.v9i2.4715

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Pengalaman positif yang didapatkan membantu individu mencoba perilaku yang lebih adaptif dan merasakan perubahan di dalam diri. Penurunan tingkat kekhawatiran dalam menghadapi kecemasan dapat diidentifikasi melalui proses kognitif yang terjadi (Dewinta & Menaldi, 2018). Sebelum intervensi, individu memiliki berbagai pikiran irasional yang menimbulkan kekhawatiran dalam memandang situasi akademik, sehingga intervensi yang telah dilakukan ini memberikan kesempatan untuk memunculkan pikiran yang rasional dan adaptif.…”

Section: Diskusiunclassified

Efektivitas terapi kelompok kognitif perilaku untuk menangani kecemasan akademik pada mahasiswa rantau

Luzanil

Menaldi

2021

JPU

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Kekhawatiran sebagai komponen dalam kecemasan akademik memengaruhi persepsi mahasiswa terhadap suatu situasi atau hasil sebagai ancaman. Mahasiswa rantau rentan terhadap kekhawatiran selama menjalani kehidupan akademik. Menurunkan kekhawatiran dan meningkatkan optimisme menjadi upaya untuk membantu mahasiswa rantau dalam menyelesaikan pendidikan. Penelitian ini bertujuan untuk mengetahui efektivitas dari CBT yang dilakukan dalam kelompok melalui media daring untuk mengurangi kekhawatiran dan meningkatkan optimism pada mahasiswa rantau dengan masalah kecemasan akademik. Terapi diberikan secara berkelompok kepada enam mahasiswa dalam lima sesi dengan tiga kali pengukuran menggunakan The Student Worry Questionnaire dan Life Orientation Test-Revised (LOT-R). Penelitian ini dianisis secara deskriptif melalui perubahan dari setiap hasil pengukuran dan analisis kualitatif yang diperoleh dari observasi dan wawancara. Hasil penelitian menunjukkan terdapat penurunan pada tingkat kekhawatiran meskipun tidak terdapat perubahan pada optimisme. Evaluasi kualitatif juga menunjukkan perubahan yang terjadi antara sebelum dan setelah intervensi. Penelitian ini menunjukkan bahwa CBT kelompok berpotensi dalam menurunkan kekhawatiran pada mahasiswa rantau dengan masalah kecemasan akademik.

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Section: Diskusiunclassified

Efektivitas terapi kelompok kognitif perilaku untuk menangani kecemasan akademik pada mahasiswa rantau

Luzanil

Menaldi

2021

JPU

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“…Throughout the clinical practice, errors in diagnosis and incorrect results sometimes arise because of the disparity between the specialists and the lack of flow. Programmed identification of arrhythmia and identifiable proof of occasions are important as experts should be helped in differentiating arrhythmic occasions beforehand [2].…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia and Disease Classification Based on Deep Learning Techniques

Franklin¹,

Muthukumar²

2022

Intelligent Automation &Amp; Soft Computing

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Electrocardiography (ECG) is a method for monitoring the human heart's electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech recognition, prediction etc., As CNNs are suitable to reduce recurrence types, LSTMs are reasonable for temporary displays and DNNs are appropriate for preparing highlights for a more divisible area. CNN, LSTM, and DNNs are appropriate to view. The complementarity of CNNs, LSTMs, and DNNs was explored in this paper by consolidating them through a single architecture firm. Our findings show that the methodology suggested can expressively explain ECG series and of detection of anomalies through scores that beat other techniques supervised as well as unsupervised technique. The LSTM-Network and FL also showed that the imbalanced data sets of the ECG beat detection issue have been consistently solved and that they have not been prone to the accuracy of ECG-Signals. The novel approach should be used to assist cardiologists in their accurate and unbiased analysis of ECG signals in telemedicine scenarios.

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“…Arrhythmia is a highly frequent cardiac ailment that is well researched and understood by specialists in the field. Throughout the course of clinical practice, mistakes in diagnosis and inaccurate outcomes may occur due to the gap in expertise between experts and the absence of a smooth flow of information [ 2 ]. Programmable detection of arrhythmias and traceable confirmation of occurrences are critical because specialists should be assisted in distinguishing between arrhythmic events before they are seen.…”

Section: Introductionmentioning

confidence: 99%

Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer

Dhanagopal¹,

Kumar²,

Alkhayyat

et al. 2022

Computational Intelligence and Neuroscience

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Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such as voice recognition and prediction, the long short-term memory (LSTM) fully connected layer (FCL) and the two convolutional neural networks (CNNs) have shown superior performance over deep learning networks (DLNs). DNNs are suitable for making high points for a more divisible region and CNNs are suitable for reducing recurrence types, LSTMs are appropriate for temporary displays, in the same way as CNNs are appropriate for reducing recurrence types. The CNN, LSTM, and DNN algorithms are acceptable for viewing. The complementarity of DNNs, CNNs, and LSTMs was investigated in this research by bringing them all together under the single architectural company. The researchers got the ECG data from the MIT-BIH arrhythmia database as a result of the investigation. Our results demonstrate that the approach proposed may expressively describe ECG series and identify abnormalities via scores that outperform existing supervised and unsupervised methods in both the short term and long term. The LSTM network and FCL additionally demonstrated that the unbalanced datasets associated with the ECG beat detection problem could be consistently resolved and that they were not susceptible to the accuracy of ECG signals. It is recommended that cardiologists employ the unique technique to aid them in performing reliable and impartial interpretation of ECG data in telemedicine settings.

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Cognitive Behavior Therapy for Generalized Anxiety Disorder: A Case Study of Arrhythmia Patient

Cited by 5 publications

References 20 publications

Efektivitas terapi kelompok kognitif perilaku untuk menangani kecemasan akademik pada mahasiswa rantau

Efektivitas terapi kelompok kognitif perilaku untuk menangani kecemasan akademik pada mahasiswa rantau

Arrhythmia and Disease Classification Based on Deep Learning Techniques

Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer

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